Ontology Engineering Group. Universidad Politécnica de Madrid
PhDSymposium paper PhDSymposium: eDocument Standards as Background Knowledge in Context-Based Ontology Matching
GESIS-Leibniz Institute for the Social Sciences
GESIS - Leibniz Institute for the Social Sciences
Leibnitz University, Research Group Human-Computer Interaction
(118) Fadi Maali, Stéphane Campinas and Stefan Decker.
Reseach paper Cognition & WebScience: Gagg: A Graph Aggregation Operator
(47) Panos Alexopoulos, Ronald Denaux and Jose Manuel Gomez-Perez.
In-Use paper InUse & Industry 2: Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry
(89) Revathy Krishnamurthy, Pavan Kapanipathi, Amit Sheth and Krishnaprasad Thirunarayan.
Reseach paper Crowd Sourcing & Web Science: Knowledge Enabled Approach to Predict the Location of Twitter Users
Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing
In this paper we present an architecture and approach to publishing open linked data in cultural heritage domain. We demonstrate our approach for building a system both for data publishing and consumption and show how user’s benefits can be achieved with semantic technologies. For domain knowledge representation CIDOC-CRM ontology is used. As a main source of trusted data the web portal of the Russian Museum is crawled. For data enrichment we selected DBpedia and SPARQL-endpoint of the British Museum. The evaluation shows potentials of semantics applications for data publishing in contextual environment, semantic search and visualization and automated enrichment according to needs and expectations of art experts and regular museum visitors.
Department of Computer Science and Eingeering, Wright State University, and Faculty of Computer Science, Universitas Indonesia
Distributed and Scalable OWL EL Reasoning
OWL 2 EL is one of the tractable profiles of Web Ontology Language (OWL) which has been standardized by the W3C. OWL 2 EL provides sufficient expressivity to model large biomedical ontologies as well as streaming data such as traffic. Automated generation of ontologies from streaming data and text can lead to very large ontologies. Existing reasoners make use of only a single machine and are thus constrained by memory and computational power. There is a need to develop reasoning approaches which scale with the size of ontologies. We describe a distributed reasoning system that scales well using a cluster of commodity machines. We also apply our system to a use case on city traffic data and show that it can handle volumes which cannot be handled by current reasonably-sized single machine reasoners.
University of Applied Sciences St. Pölten
Web and philosophy. The relationship between the Web and philosophy is now at a crucial turning point. While a group of philosophers and philosophically-influenced scholars are increasingly interested in the Web, we are facing unprecedented challenges around its future that requires concerted efforts between researcher and disciplines to be properly addressed. With both Internet governance and the very architecture of the Web undergoing rapid change, now is the time for a philosophy of the Web to help to fulfill the Web’s full potential, expanding upon its fundamental principles in new terrains ranging from mass surveillance to the impact of the Internet of things. Even swifter is the Web-driven transformation of many previously unquestioned philosophical concepts of privacy, authority, meaning, identity, belief, intelligence, cognition, and even embodiment in surprising ways. In response, we hope to provoke the properly philosophical question of whether or not philosophy that can weave these changes to technology and society into a coherent whole that can adapt the principles of the Web to the age of surveillance.
In-Use Track InUse & Industry 1: InUse & Industry 1
Dipartimento di Ingegneria Informatica Automatica e Gestionale Antonio Ruberti, University of Rome "La Sapienza"
Dipartimento di Ingegneria e Scienza dell’Informazione (DISI) - University of Trento
Reseach paper LinkedData & Data Management: Quality Assessment of Linked Datasets using Probabilistic Approximations
(125) Jeremy Debattista, Santiago Londoño, Christoph Lange and Sören Auer.
(141) Valentina Ivanova, Patrick Lambrix and Johan Åberg.
Reseach paper Ontology Matching & Summarization: Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment
IBM Research, Smarter Cities Technology Centre
Information Sciences Institute, University of Southern California
University of Applied Sciences and Arts Dortmund
Semantic Web Enterprise Adoption and Best Practice. The WaSABi workshop aims at helping to guide the conversation between the scientific research community, IT practitioners and industry. The workshop helps to establish best practices for the development, deployment and evaluation of Semantic Web based technologies. Both research and industry communities can benefit greatly from this discussion by sharing use cases, user stories, practical development issues, evalutaions and design patterns. The goal of this particular workshop is to analyse the emerging Semantic Technology market, identify and apply appropriate methodologies, highlight success stories and overall support the increase in industry adoption of Semantic Web technologies.
Building interoperable read-write Linked Data applications with the W3C Linked Data Platform and the LDP4j framework. The W3C Linked Data Platform (LDP) is an initiative to produce a standard protocol and a set of best practices for the development of read-write Linked Data applications. The LDP protocol provides clarifications on the use of the HTTP protocol in Linked Data applications, proposes several extensions in the context of Linked Data applications, and enforces further restrictions. As consequence, the LDP specification enables interoperable read-write Linked data applications based on HTTP access to web resources that describe their state using the RDF data model. The standardization of this protocol represents a step forward in the Linked Data community as it lays the ground for the development of interoperable read-write Linked Data applications. This tutorial provides an overview to the Linked Data Platform including resources, different types of containers, and other features such as paging, patching, etc., and discusses the different design considerations one should take into account when building read-write Linked Data applications. In addition to the theoretical background on the LDP specification and application design, the tutorial consists of a hands on session on how to build a read-write Linked Data application from scratch using LDP4j, an open source Java framework for developing LDP-enabled applications.
Knowledge Discovery and Data Mining Meets Linked Open Data. The workshop addresses researchers and practitioners from the fields of knowledge discovery in databases and data mining, as well as researchers from the Semantic Web community applying such techniques to Linked Data. The goal of the workshop is to provide a platform for knowledge exchange between the different research communities, and to foster future collaborations. We expect at least 30 participants. Authors of contributed papers are especially encouraged to publish their data sets and/or the implementation of their algorithms, and to discuss these implementations and data sets with other attendees. The goal is to establish a common benchmark that can be used for competitive evaluations of algorithms and tools.
Semantic Web for Scientific Heritage. The purpose of the workshop is to provide a forum for discussion about the methodological approaches to the specificity of annotating “scientific” texts (in the wide sense of the term, including disciplines such as history, architecture, or rhetoric), and to support a collaborative reflection, on possible guidelines or specific models for building historical ontologies. A key goal of the workshop is to emphasize, through precise projects and up-to-date investigation in digital humanities, the benefit of a multidisciplinary research to create and operate on relevantly structured data. One of the main interests of the very topic of pre-modern historical data management lies in historical semantics, and the opportunity to jointly consider how to identify and express lexical, theoretical and material evolutions. Dealing with historical material, a major problem is indeed to handle the discrepancy of the historical terminology compared to the modern one, and, in the case of massive, diachronic data, to take into account the contextual and theoretical meaning of words and sentences and their semantics. Papers on ancient and medieval biological science and zoology are particularly welcome.
Using the Web in the Age of Data. The purpose of the USEWOD workshop series has been to create and maintain a forum for researchers to investigate the synergy between the Web of Data and Web usage mining. This required the analysis of semantic data usage. With the next edition we respond to the fact that publishing and consuming raw data on the Web is an established paradigm today and turn the USEWOD workshop into a forum to discuss more general questions about the usage of the Web. How will the analysis of Web usage benefit from the possibility to blend the classical log with the structure sourcing from Linked Data? Can the progress that has been made on (Read/Write) Linked Data change the way we interact with the Web and what does that mean for the usage analysis capabilities we have at hand today?
Dipartimento di Informatica, Università degli Studi di Bari
While individual components for semantic data integration are commonly available, end-to-end solutions are rare. We demonstrate DataOps, a seamless Anything-to-RDF semantic data integration toolkit. DataOps supports the integration of both semantic and non-semantic data from an extensible host of different formats. Setting up data sources end-to-end works in three steps: (1) accessing the data from arbitrary locations in different formats, (2) specifying map- pings depending on the data format (e.g., R2RML for relational data), and (3) consolidating new data with existing data instances (e.g., by establishing owl:sameAs links). All steps are supported through a fully integrated Web interface with configuration forms and different mapping editors. Visitors of the demo will be able to perform all three steps of the integration process.
DataOps: Seamless End-to-end Anything-to-RDF Data Integration
(171) Georgia Troullinou, Haridimos Kondylakis, Evangelia Daskalaki and Dimitris Plexousakis.
Reseach paper Ontology Matching & Summarization: RDF Digest: Efficient Summarization of RDF/S KBs
Reseach paper Natural Language Processing: LIME: the Metadata Module for OntoLex
(151) Manuel Fiorelli, Armando Stellato, John P. Mccrae, Philipp Cimiano and Maria Teresa Pazienza.
INSERM UMRS 872, éq.20, 15, rue de l’école de médecine, 75006 Paris
A Context-Based Semantics for SPARQL Property Paths over the Web
As of today, there exists no standard language for querying Linked Data on the Web, where navigation across distributed data sources is a key feature. A natural candidate seems to be SPARQL, which recently has been enhanced with navigational capabilities thanks to the introduction of property paths (PPs). However, the semantics of SPARQL restricts the scope of navigation via PPs to single RDF graphs. This restriction limits the applicability of PPs on the Web. To fill this gap, in this paper we provide formal foundations for evaluating PPs on the Web, thus contributing to the definition of a query language for Linked Data. In particular, we introduce a query semantics for PPs that couples navigation at the data level with navigation on the Web graph. Given this semantics we find that for some PP-based SPARQL queries a complete evaluation on the Web is not feasible. To enable systems to identify queries that can be evaluated completely, we establish a syntactic and, thus, decidable property of such queries.
PhDSymposium paper PhDSymposium: Ontology Change in Ontology-Based Information Integration Systems
Standardized and Efficient RDF Encoding for Constrained Embedded Networks
In the context of Web of Things (WoT), embedded networks have to face the challenge of getting ever more complex. The complexity arises as the number of interchanging heterogeneous devices and different hardware resource classes always increase. When it comes to the development and the use of embedded networks in the WoT domain, Semantic Web technologies are seen as one way to tackle this complexity. For example, properties and capabilities of embedded devices may be semantically described in order to enable an effective search over different classes of devices, semantic integration may be deployed to integrate data produced by these devices, or embedded devices may be empowered to reason about semantic data in the context of a WoT application. Despite these possibilities, a wide adoption of Semantic Web or Linked Data technologies in the domain of embedded networks has not been established yet. One reason for this is an inefficient representation of semantic data. Serialisation formats of RDF data, such as for instance a plain-text XML, are not suitable for embedded devices. In this paper, we present an approach that enables constrained devices, such as microcontrollers with very limited hardware resources, to store and process semantic data. Our approach is based on the W3C Efficient XML Interchange (EXI) format. To show the applicability of the approach, we provide an EXI-based RDF Store and show associated evaluation results.
RDF Stream Processing. Data streams are an increasingly prevalent source of information in a wide range of domains and applications, e.g. environmental monitoring, disaster response, or smart cities. The RDF model is based on a traditional persisted-data paradigm, where the focus is on maintaining a bounded set of data items in a knowledge base. This paradigm does not fit the case of data streams, where data items flow continuously over time, forming unbounded sequences of data. In this context, the W3C RDF Stream Processing (RSP) Community Group has taken the task to explore the existing technical and theoretical proposals that incorporate streams to the RDF model, and to its query language, SPARQL. More concretely, one of the main goals of the RSP Group is to define a common, but extensible core model for RDF stream processing. This core model can serve as a starting point for RSP engines to be able to talk to each other and interoperate. The goal of this workshop is to bring together interested members of the community to: (i) Demonstrate their latest advances in stream processing systems for RDF. (ii) Foster discussion for agreeing on a core model and query language for RDF streams. (iii) Involve and attract people from related research areas to actively participate in the RSP Community Group.
Reseach Track Semantic Data Management & Big Data 1: Semantic Data Management & Big Data 1
Universidad Nacional del Sur and CONICET
SentiML ++ : An Extension of the SentiML Sentiment Annotation Scheme
DataWeb Research, Department of Computer Science, University of Valladolid
PhDSymposium paper PhDSymposium: Semantics-enabled User Interest Mining
Theoretical Computer Science, TU Dresden
Evaluating Entity Annotators Using GERBIL
The need to bridge between the unstructured data on the Document Web and the structured data on the Web of Data has led to the development of a considerable number of annotation tools. However, these tools are hard to compare due to the diversity of data sets and measures used for evaluation. We will demonstrate GERBIL, an evaluation framework for semantic entity annotation that provides developers, end users and researchers with easy-to-use interfaces for the agile, fine-grained and uniform evaluation of annotation tools on 11 different data sets within 6 different experimental settings on 6 different measures.
Reseach paper Semantic Data Management & Big Data 1: Query Execution Optimization for Clients of Triple Pattern Fragments
(54) Joachim Van Herwegen, Ruben Verborgh, Erik Mannens and Rik Van de Walle.
Reseach paper Cognition & WebScience: LOD-based Disambiguation of Named Entities in @tweets through Context #enrichment
(191) Genevieve Gorrell, Johann Petrak and Kalina Bontcheva.
Intelligent and Efficient Techniques for RDF Query Relaxation
This paper addresses the problem of failing RDF queries. Query relaxation is one of the cooperative techniques that allows providing user with alternative answers instead of an empty result. While previous work on query relaxation over RDF data have focused on defining new relaxation operators, in this paper techniques to find the parts of an RDF query which are responsible of its failure, are investigated. Finding such subqueries, named Minimal Failing Subqueries (MFS), is of great interest to efficiently perform the relaxation process. We propose two algorithmic approaches inspired by previous work conducted in the context of relational databases. The first approach (called LBA) intelligently leverages the lattice of subqueries of the initial RDF query while the second (called MBA) is based on a particular matrix that can be used to improve the performance of LBA. In addition, we also show how our approaches can compute maximal non-failing subqueries, called Maximal Success Subqueries (XSS). XSSs are subqueries with maximal number of triple patterns of the initial query that result in non-empty answers. To validate our approaches, a set of thorough experiments is conducted on the LUBM benchmark and a comparative study with other approaches is done.
Dipartimento Informatica Università "Aldo Moro" Bari
The Web of Data for E-Commerce for Researchers and Practitioners. In this tutorial, we will (1) give a comprehensive overview and hands-on training on the conceptual structures of schema.org for e-commerce, including patterns for ownership and demand, (2) present the full tool chain for producing and consuming respective data, (3) explain the long-term vision of linked open commerce, (4) describe the main challenges for future research in the field, and (5) discuss advanced topics, like access control, identity and authentication (e.g. with WebID), micropayment services (like Payswarm), and data management issues from the publisher and consumer perspective. We will also cover research opportunities resulting from the growing adoption and the respective amount of data in RDFa and Microdata syntaxes.
Reseach paper Reasoning: A Context-Based Semantics for SPARQL Property Paths over the Web
PhDSymposium paper PhDSymposium: Creating Learning Material from Web Resources
Universidade Federal de Pernambuco (UFPE)
PhDSymposium paper PhDSymposium: Semantic support for recording laboratory experimental metadata: a study in Food Chemistry
The 12th Extented Semantic Web Conference
Grand hotel Bernardin, Obala 2, 6320 Portorož, Slovenia
The use of knowledge bases have been shown to improve performance in applications ranging from web search and event detection to entity recognition and disambiguation. More recently, knowledge bases have been used to address challenges in analyzing social data. A key challenge in this domain has been that of identifying the geographic footprint of online users in a social network such as Twitter. Existing approaches to predict the location of users, based on their tweets, solely rely on social media features or probabilistic language models. These approaches are purely data-driven and require large training dataset of geo-tagged tweets to build statistical models that predict the location of a user. As most Twitter users are reluctant to publish their location, the collection of geo-tagged tweets is a time intensive process. To address this issue, we present an alternative, knowledge-based approach to predict a Twitter user's location at the city level. We utilize Wikipedia as the source of our knowledge base by exploiting its hyperlink structure which alleviates the dependence on training data set. Our experiments, on a publicly available dataset demonstrate an improvement of 3\% in the accuracy of prediction, over the state of the art supervised techniques.
Knowledge Enabled Approach to Predict the Location of Twitter Users
Japan Advanced Institute of Science and Technology
Using Ontologies For Modeling Virtual Reality Scenarios
Serious games with 3D interfaces are Virtual Reality (VR) systems that are becoming common for the training of military and emergency teams. A platform for the development of serious games should allow the addition of semantics to the virtual environment and the modularization of the artificial intelligence controlling the behaviors of non-playing characters in order to support a productive end-user development environment. In this paper, we report the ontology design activity performed in the context of the PRESTO project aiming to realize a conceptual model able to abstract the developers from the graphical and geometrical properties of the entities in the virtual reality, as well as the behavioral models associated to the non-playing characters. The feasibility of the proposed solution has been validated through real-world examples and discussed with the actors using the modeled ontologies in every day practical activities.
Distributed Linked Data Business Communication Networks: The LUCID Endpoint
With the LUCID Endpoint, we demonstrate how companies can utilize Linked Data technology to provide major data items for their business partners in a timely manner, machine readable and with open and extensible schemata. The main idea is to provide a Linked Data infrastructure which enables all partners to fetch, as well as to clone and to synchronize datasets from other partners over the network. This concept allows for building of networks of business partners much like as social network but in a distributed manner. It furthermore provides a technical infrastructure for business communication acts such as supply chain communication or master data management.
(83) Xiaoqi Cao, Patrick Kapahnke and Matthias Klusch.
Reseach paper Mobile Sensors, Services & Web of Things: SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks
EU Networking posters will be shown during afternoon coffee break.
This paper explores the factors that influence the performance of hybrid named entity recognition (NER) approaches to microblogs, which combine state-of-the-art automatic techniques with human and crowd computing. We identify a set of content and crowdsourcing-related features (number of entities in a post, types of entities, skipped true-positive posts, average time spent to complete the tasks, and interaction with the user interface) and analyze their impact on the accuracy of the results and the timeliness of their delivery. Using CrowdFlower and a simple, custom built gamified NER tool we run experiments on three datasets from related literature and a fourth newly annotated corpus. Our findings show that crowd workers are adept at recognizing people, locations, and implicitly identified entities within shorter microposts. We expect them to lead to the design of more advanced NER pipelines, informing the way in which tweets are chosen to be outsourced or processed by automatic tools. Experimental results are published as JSON-LD for further use by the research community.
Towards hybrid NER: a study of content and crowdsourcing-related performance factors
Hackfest half day event: Microcontrollers
(20) Achim Rettinger, Artem Schumilin, Steffen Thoma and Basil Ell.
Reseach paper Natural Language Processing: Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text
Ontology change is an important part of the Semantic Web field that helps researchers and practitioners to deal with changes performed in ontologies. The topic is especially important in Ontology-Based Information Integration (OBII) systems, where several ontologies are interrelated and therefore, changes raise various complexities and implications, such as modifications of ontology mappings and change propagation. Current approaches to ontology change are mainly focused on a single ontology and therefore they do not properly address the constraints specific to OBII systems. To address the challenge of ontology change in OBII contexts, we are planning to adapt successful techniques proposed by both Semantic Web and Model-Driven Engineering communities. We discuss the research goals, methods, and evaluation options to address this challenge. Real-world OBII case studies are used for the development and evaluation of the proposed methods.
Ontology Change in Ontology-Based Information Integration Systems
We present an architecture and approach to publishing open linked data in the cultural heritage domain. We demonstrate our approach for building a system both for data publishing and consumption and show how user benefits can be achieved with semantic technologies.For domain knowledge representation the CIDOC-CRM ontology is used.As a main source of trusted data, we use the data of the web portal of the Russian Museum. For data enrichment we selected DBpedia and the published Linked Data of the British Museum. Our work can be reached at www.culturecloud.ru
HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce
HDT a is binary RDF serialization aiming at minimizing the space overheads of traditional RDF formats, while providing retrieval features in compressed space. Several HDT-based applications, such as the recent Linked Data Fragments proposal, leverage these features for diverse publication, interchange and consumption purposes. However, scalability issues emerge in HDT construction because the whole RDF dataset must be processed in a memory-consuming task. This is hindering the evolution of novel applications and techniques at Web scale. This paper introduces HDT-MR, a MapReduce-based technique to process huge RDF and build the HDT serialization. HDT-MR performs in linear time with the dataset size and has proven able to serialize datasets up to 4.42 billion triples, preserving HDT compression and retrieval features. These results enable a new generation of HDT-based applications.
Recently there is an increasingly interest on using socially generated media contents in the disasters domain. This fact has been empowered by the successful use of this type of data for early detection of earthquakes or helping to communicate efficiently during the management of disasters. This paper presents a system that allows on real time to analyze, enrich, and detect needs from a set of web sources. This tool can help enormously for early detection and for fast response to currently demanded needs during the occurrence of a disaster.
Center for Limnology - University of Wisconsin
The Design and Implementation of Semantic Web-based Architecture for Augmented Reality Browser
Due to the proliferation of smartphones, Augmented Reality applications have become more widespread nowadays. Augmented Reality browsers have especially enjoyed wide popularity within these applications. The physical environment could be extended by location-aware additional information using these browsers. At present, typically a specific data source is used by the current Augmented Reality browsers, even if there is an enormous amount of available data sources. The Semantic Web could help to bridge this problem. The goal of this work is to combine the Augmented Reality and Semantic Web technologies in order to enhance the existing mobile Augmented Reality browsers using Semantic Web technologies. For this purpose, we utilize the advantages of Semantic Web technologies such as data integration, unified data model as well as publicly available semantic data sources, among other things.
In-Use paper InUse & Industry 1: From Symptoms to Diseases -- Creating the Missing Link
(68) Heiner Oberkampf, Turan Gojayev, Sonja Zillner, Dietlind Zühlke, Sören Auer and Matthias Hammon.
Enterprise Architecture (EA) models are established means for decision makers in organizations. They describe the business processes, the application landscape and IT infrastructure as well as the relationships between those layers. Current research focuses merely on frameworks, modeling and documentation approaches for enterprise architectures. But once these models are established, methods for their analysis are rare. In this paper we propose the use of semantic web technologies in order to represent the enterprise architecture and perform analyses. We present an approach how to transform an existing EA model into an ontology. Using this knowledge base, simple questions can be answered with the query language SPARQL. The major benefits of semantic web technologies can be found, when defining and applying more complex analysis. Change impact analysis are important to estimate the effects and costs of a change to an EA model element. To show the benefits of semantic web technologies for EA, we implemented an approach to change impact analysis and executed it within a case study.
Using semantic web technologies for enterprise architecture analysis
In the process of creating ontologies, it is widely accepted to reuse terms in existing well-known ontologies. Some recommendations exist to guide the ontologists in ontology engineering, but still hard to find a tool that provide users with an environment to reuse terms. This paper presents ProtégéLOV, a plugin for the ontology editor Protege that combine the access to the Linked Open Vocabularies (LOV) during ontology modeling. It search a term in LOV and provide three actions if the term exists : (i) replace the selected term in the current ontology, (ii) add the rdfs:subClassOf axiom and (iv) add the owl:equivalentClass. Results from a preliminary user study indicate that ProtégéLOV does provide an intuitive access and reuse of terms in external vocabularies.
The ProtégéLOV Plugin: Ontology Access and Reuse for Everyone
The Food and Agricultural Organization of UN (FAO)
Department of Computer Science, University of Cape Town
In-Use Track InUse & Industry 3: InUse & Industry 3
3XL News: a Cross-lingual News Aggregator and Reader
We present 3XL News, a multi-lingual news aggregation application for iPad that provides real-time, comprehensive, global and multilingual news coverage. Using methods, developed within the XLike project, for semantic data extraction from news articles and linking of news stories we are able to construct a concise, yet in-depth view of current news stories and their semantic relation. This enables users real-time monitoring of current global events and analysis of diverse reporting in different languages and navigation across related news stories.
Reseach Track Machine Learning: Machine Learning
Instance matching concerns identifying pairs of instances that refer to the same underlying entity. Current state-of-the-art instance matchers use machine learning methods. Supervised learning systems achieve good performance by training on significant amounts of manually labeled samples. To alleviate the labeling effort, this poster presents a minimally supervised instance matching approach that is able to deliver competitive performance using only 2% training data. As a first step, a committee of base classifiers is trained in an ensemble setting using boosting. Iterative semi-supervised learning is used to improve the performance of the ensemble classifier even further, by self-training it on the most confident samples labeled in the current iteration. Empirical evaluations on real-world data show that, using a multilayer perceptron as base classifier, the system is able to achieve an average F-Measure that is within 2.5% of that of state-of-the-art supervised systems.
Minimally Supervised Instance Matching: An Alternate Approach
We present a web-based tool that shows a summary of an RDF dataset as a visualization of a graph formed from classes, datatypes and predicates used in the dataset. The visualization should allow to quickly and easily find out what kind of data the dataset contains and its structure. It also shows how vocabularies are used in the dataset.
Dataset Summary Visualization with LODSight
Reseach paper Ontologies: RODI: A Benchmark for Relational-to-Ontology Data Integration
(72) Christoph Pinkel, Carsten Binnig, Ernesto Jiménez-Ruiz, Wolfgang May, Dominique Ritze, Martin G. Skjaeveland and Alessandro Solimando.
Ontology Engineering Group - Universidad Politécnica de Madrid
FAGI-gis: A tool for fusing geospatial RDF data
In this demonstration, we present FAGI-gis, a tool for fusing geospatial RDF data. FAGI-gis is the core component of the FAGI framework, which handles all the steps of the fusion process of two interlinked RDF datasets in order to produce an integrated, aligned and richer dataset that combines data and metadata from both initial datasets. In the demonstation, we showcase how a user can use FAGI-gis’s map based UI to perform several fusion actions on linked geospatial entities, considering both spatial and non-spatial properties of them.
Harvard Medical School \& Massachusetts General Hospital
Interactive Comparison of Triple Pattern Fragments Query Approaches
In order to reduce the server-side cost of publishing queryable Linked Data, Triple Pattern Fragments (tpf) were introduced as a~simple interface to rdf triples. They allow for sparql query execution at low server cost, by partially shifting the load from servers to clients. The previously proposed query execution algorithm provides a solution that is highly inefficient, often requiring an amount of http calls that is magnitudes larger than the optimal solution. We have proposed a new query execution algorithm with the aim to solve this problem. Our solution significantly improves on the current work by maintaining a complete overview of the query instead of just looking at local optima. In this paper, we describe a demo that allows a user to easily compare the results of both implementations. We show both query results and number of executed http calls, proving a clear picture of the difference between the two algorithms.
Social: Gala dinner and local band at the Grand Hotel Bernardin Beach
PhDSymposium paper PhDSymposium: Information Extraction for Learning Expressive Ontologies
Intelligent Software Components (iSOCO) S.A.
Tutorial on Enterprise Linked Data Strategies & the Commercialization of Interlinked Data (ELDSCID 2015). The adoption of Linked Data technologies in enterprises in order to improve methods of Data Integration and to introduce new approaches for a more efficient Information Management is becoming increasingly popular. This trend is complemented by activities aimed at commercializing Interlinked (Open) Data, mainly undertaken by publishers and media companies. While professional information providers work on Linked Data infrastructures to be able to deliver better linkable information assets on a more granular level, enterprises are in the process of redesigning information strategies and content workflows. Ultimate goals are heading towards a more efficient reuse and enrichment of knowledge assets and the personalisation of information services. In this tutorial, we will give several demos and concrete examples of how Linked Data can be used by enterprises in various industries. We will classify those to give a coherent picture over ‘typical application scenarios’ and corresponding benefits arguments. From an information provider’s perspective, we will discuss how Linked Data has become a game changer for the whole content industry. In addition, we will shed light on new business models for Linked Data at the intersection of Linked Data assets, stakeholders and revenue models by introducing the Linked Data Business Cube.
When analyzing statistical data, one of the most basic and at the same time widely used techniques is analyzing correlations. As shown in previous works, Linked Open Data is a rich resource for discovering such correlations. In this demo, we show how statistical analysis and visualization on maps can be combined to allow a deeper understanding of the statistical findings.
Visual Analysis of Statistical Data on Maps using Linked Open Data
Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS)
Hasso-Plattner-Institut, Universität Potsdam
Large scale rule-based Reasoning using a Laptop
Although recent developments have shown that it is possible to reason over large RDF datasets with billions of triples in a scalable way, the reasoning process can still be a challenging task with respect to the growing amount of available semantic data. By now, reasoner implementation that are able to process large scale datasets usually use a MapReduce based implementation that runs on a cluster of computing nodes. In this paper we address this circumstance by identifying the resource consuming parts of a reasoner process and providing a solution for a more efficient implementation in terms of memory consumption. As a basis we use a rule-based reasoner concept from our previous work. In detail, we are going to introduce an approach for a memory efficient RETE algorithm implementation. Furthermore, we introduce a compressed triple-index structure that can be used to identify duplicate triples and only needs a few bytes to represent a triple. Based on these concepts we show that it is possible to apply all RDFS rules to more than 1 billion triples on a single laptop reaching a throughput, that is comparable or even higher than state of the art MapReduce based reasoner. Thus, we show that the resources needed for large scale lightweight reasoning can massively be reduced.
Hasso-Plattner-Institute for IT Systems Engineering, University of Potsdam
In-Use paper InUse & Industry 2: Using Ontologies For Modeling Virtual Reality Scenarios
(105) Mauro Dragoni, Chiara Ghidini, Paolo Busetta, Mauro Fruet and Matteo Pedrotti.
Goethe-University Frankfurt am Main, Text Technology Group
Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment
Currently one of the challenges for the ontology alignment community is the user involvement in the alignment process. At the same time, the focus of the community has shifted towards large-scale matching which introduces an additional dimension to this issue. This paper aims to provide a set of requirements that foster the user involvement for large-scale ontology alignment tasks. Further, we present and discuss the results of a literature study for 7 ontology alignments systems as well as a heuristic evaluation and an observational user study for 3 ontology alignment systems to reveal the coverage of the requirements in the systems and the support for the requirements in the user interfaces.
Services and Applications over Linked APIs and Data. The World Wide Web has undergone significant changes, developing from a collection of a few interlinked static pages to a global ubiquitous platform for sharing, searching and browsing dynamic and customisable content, in a variety of different media formats. This transformation was triggered by the ever growing number of users and websites and continues to be supported by current developments such as the increased use and popularity of Linked Data and Web APIs. Unfortunately, despite some initial efforts and progress towards integrated use, these two technologies remain mostly disjunct in terms of developing solutions and applications. To this purpose, SALAD aims to explore the possibilities of facilitating a better fusion of Web APIs and Linked Data, thus enabling the harvesting and provisioning of data through applications and services on the Web. In particular, we focus on investigating how both static and dynamic resources (for example, sensor data or streams), exposed via interfaces on the Web, can be used together with semantic data, as a means for enabling a shared use and providing a basis for developing rich applications. With this workshop, we aim to discover new ways to embrace the opportunities that Web APIs offer in terms of data consumption, processing and provisioning but also to investigate the possibilities of integrating them more closely with Linked Data. We want to challenge researchers towards developing integrated description and implementation approaches through both paper submissions and interactive on-site discussion and dialog. In particular, we are looking for description approaches, implementation solutions, use cases and applications that support a more automated and unified Web API use.
Ontology learning (OL) aims at the (semi-)automatic acquisition of ontologies from sources of evidence, typically domain text. Recently, there has been a trend towards the application of multiple and heterogeneous evidence sources in OL. Heterogeneous sources provide benefits, such as higher accuracy by exploiting redundancy across evidence sources, and including complementary information. When using evidence sources which are heterogeneous in quality, amount of data provided and type, then a number of questions arise, for example: How many sources are needed to see significant benefits from heterogeneity, what is an appropriate number of evidences per source, is balancing the number of evidences per source important, and to what degree can the integration of multiple sources overcome low quality input of individual sources? This research presents an extensive evaluation based on an existing OL system. It gives answers and insights on the research questions posed for the OL task of concept detection, and provides further hints from experience made. Among other things, our results suggest that a moderate number of evidences per source as well as a moderate number of sources resulting in a few thousand data instances are sufficient to exploit the benefits of heterogeneous evidence integration.
Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning
Desperately searching for travel offers? Formulate better queries with some help from Linked Data.
Various studies have reported on inefficiencies of existing travel search engines, and user frustration generated through hours of searching and browsing, often with no satisfactory results. Not only do the users fail to find the right offer in the myriad of websites, but they end up browsing through many offers that do not correspond to their criteria. The Semantic Web framework is a reasonable candidate to improve this. In this paper, we present a semantic travel offer search system named “RE-ONE”. We especially highlight its ability to help users formulate better search queries. An example of a permitted query is in Croatia at the seaside where there is Vegetarian Restaurant. To our best knowledge, our system is the first search system that leverages the semantic graph to construct indexes and to support the cognitive process of travel search. We conducted two experiments to evaluate the Query Auto-completion mechanism. The results showed that our system outperforms the Google Custom Search baseline. Queries freely conducted in RE-ONE are shown to be 63.4% longer and 27% richer in terms of number of search criteria. RE-ONE supports better users’ cognitive process by giving suggestions in greater accordance with users’ idea flow.
Dipartimento di Informatica - University of Bari
PhDSymposium paper PhDSymposium: The Design and Implementation of Semantic Web-based Architecture for Augmented Reality Browser
Query Execution Optimization for Clients of Triple Pattern Fragments
In order to reduce the cost of publishing queryable Linked Data, Triple Pattern Fragments (TPF) were introduced as a~simple interface to RDF triples. It allows for SPARQL query execution at low server cost, by partially shifting the load from the server to the client. The previously proposed client algorithm uses more HTTP requests than strictly necessary, and only makes partial use of the available metadata. In this paper, we propose a new query execution algorithm for a client communicating with a TPF server. Instead of using a greedy solution, we maintain an overview of the entire query to find the steps that are optimal for solving a given query. We show multiple cases in which our implementation produces a solution containing far fewer server calls, while not significantly increasing the cost in other cases. This improves the efficiency of common SPARQL queries against TPF interfaces, augmenting their viability compared to the more powerful, but more costly, SPARQL interface.
Information collected from streams of data, generated by different types of sensors, is becoming an increasingly important aspect in criminal intelligence. Monitoring potentially relevant activities in ongoing criminal investigations is time-consuming, and dynamic data often has to logged for later use, missing out on the potential benefit of detecting events in real-time. In this paper we present an approach for creating a decoupled semantically enabled event processing system, leveraging existing Semantic Web technologies, and demonstrate its applicability and scalability in a small use-case scenario.
Supporting Real-Time Monitoring in Criminal Investigations
How to develop mobile Linked Data apps in 10 mins! With the rapid advancement of mobile technologies, people are using their mobile devices as primary computing platforms and are generating and consuming vast amounts of data on them. However, the lack of Linked Data tools for these devices has left much of this data unstructured and unfit for reuse or integration with other datasets. In this paper, we present Punya, an open source platform that enables easy and rapid development of mobile Linked Data apps. The platform also enables app users to contribute linked data, as well as consume existing linked data sets, paving the path to a burgeoning linked data ecosystem.
Tutorial full day tutorial: Mobile LD App
Aalto University and University of Helsinki
Reseach paper Mobile Sensors, Services & Web of Things: Data as a Service: The Semantic Web Redeployed
(70) Laurens Rietveld, Ruben Verborgh, Wouter Beek, Miel Vander Sande and Stefan Schlobach.
In-Use paper InUse & Industry 2: Using semantic web technologies for enterprise architecture analysis
(111) Maximilan Osenberg, Melanie Langermeier and Bernhard Bauer.
This paper gives an overview of the Organic Data Science framework, a new approach for scientific collaboration that opens the science process and exposes information about shared tasks, participants, and other relevant entities. The framework enables scientists to formulate new tasks and contribute to tasks posed by others. The framework is currently in use by a science community studying the age of water, and is beginning to be used by others.
A Semantic, Task-Centered Collaborative Framework for Science
Heuristics for Fixing Common Errors in Deployed schema.org Microdata
Being promoted by major search engines such as Google, Yahoo!, Bing, and Yandex, Microdata embedded in web pages, especially using schema.org, has become one of the most important markup languages for the Web. However, actually deployed Microdata is most often not free from errors, which limits its practical use. In this paper, we use the WebDataCommons corpus of Microdata extracted from more than 250 million web pages for a quantitative analysis of common mistakes in Microdata provision. Since it is unrealistic that data providers will fix all the mistakes and provide clean and correct data, we discuss a set of heuristics that can be applied on the data consumer side to fix many of those mistakes in a post-processing step. We apply those heuristics to provide an improved Microdata corpus.
Managing the Evolution and Preservation of the Data Web. There is a vast and rapidly increasing quantity of scientific, corporate, government and crowd-sourced data published on the emerging Data Web. Open Data are expected to play a catalyst role in the way structured information is exploited in the large scale. This offers a great potential for building innovative products and services that create new value from already collected data. It is expected to foster active citizenship (e.g., around the topics of journalism, greenhouse gas emissions, food supply-chains, smart mobility, etc.) and world-wide research according to the fourth paradigm of science. The most noteworthy advantage of the Data Web is that, rather than documents, facts are recorded, which become the basis for discovering new knowledge that is not contained in any individual source, and solving problems that were not originally anticipated. In particular, Open Data published according to the Linked Data Paradigm are essentially transforming the Web into a vibrant information ecosystem. Preserving linked open datasets poses a number of challenges, mainly related to the nature of the LOD principles and the RDF data model. In LOD, datasets representing real-world entities are structured. Thus, in LOD, when managing and representing facts we need to take into consideration possible constraints that may hold. Since resources might be interlinked, effective citation measures are required to be in place to enable, for example, the ranking of datasets according to their measured quality. Another challenge is to determine the consequences that changes to one LOD dataset may have to other datasets linked to it. The distributed nature of LOD datasets furthermore makes archiving a headache. The first DIACHRON workshop aims at addressing the above mentioned challenges and issues by providing a forum for researchers and practitioners who apply linked data technologies to discuss, exchange and disseminate their work. More broadly, this forum will enable communities interested in data, knowledge and ontology dynamics to network and cross-fertilise. The workshop will also serve as a platform for disseminating results of the DIACHRON EU FP7 project (managing the evolution and preservation of the Data Web).
Inductive Classification through Evidence-based Models and their Ensembles
In the context of Semantic Web, one of the most important issue related to the class-membership prediction task (by using inductive models) on ontological knowledge bases concerns with the class-imbalance of the training examples, mostly due to the heterogeneous nature and the incompleteness of the knowledge bases. An ensemble learning approach has been proposed to cope with this problem. However, the majority voting procedure, exploited for deciding the membership, does not consider explicitly the uncertainty and the conflict that may occur among the classifiers of an ensemble model. Moving from this observation, we propose to integrate the Dempster-Shafer (DS) Theory with ensemble learning by exploiting DS pooling operators for combining conflictual information. Specifically, we propose an algorithm for learning Evidential Terminological Random Forest models to be used for the class-membership prediction task. The algorithm extends Terminological Random Forest in the settings of the Dempster-Shafer Theory. An empirical evaluation showed that the resulting models performs better for datasets with a lot of positive and negative examples and have a less conservative behavior than the voting-based forests.
In this paper, we propose a model for enabling users to search RDF data via keywords, thus, allowing them to discover relevant information without using complicated queries or knowing the underlying ontology or vocabulary. We aim at exploiting the characteristics of the RDF data to increase the quality of the ranked query results. We consider different dimensions for evaluating the value of results and achieving relevance, personalization and diversity.
Keyword Search on RDF Graphs: It Is More Than Just Searching for Keywords
With the adoption of RDF across several domains come growing requirements pertaining to the completeness and quality of RDF datasets. Currently, this problem is most commonly addressed by manually devising means to enriching an input dataset. The few tools that aim at supporting this endeavour usually focus on supporting the manual definition of enrichment pipelines. In this paper, we present a supervised learning approach based on a refinement operator for enriching RDF datasets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against 8 manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples.
Automating RDF Dataset Transformation and Enrichment
(142) Hamid R. Bazoobandi, Steven De Rooij, Jacopo Urbani, Annette Ten Teije, Frank Van Harmelen and Henri Bal.
Reseach paper Semantic Data Management & Big Data 2: A Compact In-Memory Dictionary for RDF data
Crowdmapping Digital Social Innovation with Linked data
The European Commision recently became interested in mapping digital social innovation in Europe. In order to understand this rapidly developing if little known area, a visual and interactive survey was made in order to crowd-source a map of digital social innovation, available at http://digitalsocial.eu. Over 900 organizations participated, and Linked Data was used as the backend with a Ruby on Rails Framework. The data was processed using SPARQL and network analysis, and a number of concrete policy recommendations resulted from the analysis.
National and Kapodistrian University of Athens
We observed that learners use general Web resources as learning material. In order to overcome problems such as distraction and abandonment of a given learning task, we want to integrate these Web resources into Web-based learning systems and make them available as learning material within the learning context. We present an approach to generate learning material from Web resources, that extracts a semantic fingerprint for these resources, obtains educational objectives, and publishes them as Linked Data.
Creating Learning Material from Web Resources
Poznan Supercomputing and Networking Center
Reseach Track Mobile Sensors, Services & Web of Things: Mobile Sensors, Services & Web of Things
Vienna University of Economy and Business (WU)
(41) Mayank Kejriwal and Daniel P. Miranker.
Reseach paper Ontology Matching & Summarization: Semi-supervised Instance Matching Using Boosted Classifiers
eDocument Standards as Background Knowledge in Context-Based Ontology Matching
Ontology matching is the process of finding correspondence between heterogeneous ontologies and consequently support semantic interoperability between different information systems. Using contextual information relative to the ontologies being matched is referred to as context-based ontology matching and is considered one promising direction of improving the matching performance. This PhD investigates how such contextual information can be best put to use, which sources of context yield the most promising results, and how such application impacts on the performance of ontology matching operations. As a part of this work we have investigated how international eDocument standards in the transport logistics domain can provide useful context when matching domain ontologies for this particular domain. The rationale is that such standards are developed in close collaboration between standards developing organizations and industry representatives and should contain useful and valid domain expertise that should lend itself well to be used as background knowledge in context-based ontology matching. A method for transforming from the standards to an ontology has been developed. Using this method an ontology from the Universal Business Language standard has been created from the XSD schemas representing UBL eDocument standards. This ontology constitutes background knowledge that can support the ontology matching operation. Furthermore, a reference alignment between two transport logistics ontologies has been developed in order to evaluate how this ontology impacts on the ontology matching process. Early evaluation tests suggest that the current version of the UBL ontology does not impact on the matching results and that further refinement of the transformation method is needed. The next steps include to run more elaborate evaluations to see how the background knowledge ontology actually influence on the ontology matching of these two ontologies and perform the required reconfiguration of the methods already developed.
Local Organization and Conference Administration
Information Extraction for Learning Expressive Ontologies
Ontologies are used to represent knowledge in a formal and unambiguous way, facilitating its reuse and sharing among people and computer systems. A large amount of knowledge is traditionally available in unstructured text sources and manually encoding their content into a formal representation is costly and time-consuming. Several methods have been proposed to support ontology engineers in the ontology building process, but they mostly turned out to be inadequate for building rich and expressive ontologies. We propose some concrete research directions for designing an effective methodology for semi-supervised ontology learning. This methodology will integrate a new axiom extraction technique which exploits several features of the text corpus.
Reseach Track Crowd Sourcing & Web Science: Crowd Sourcing & Web Science
(90) Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo and Jens Lehmann.
Reseach paper Machine Learning: Automating RDF Dataset Transformation and Enrichment
Two important characteristics of science are the “reproducibility” and “Transparency”. The clarity, complementing reproducibility, provides unambiguous descriptions of methods. Both pillars depend on well structured and accurate descriptions of scientific method, which are normally reported in methodology descriptions of scientific papers. Our main assumption is that, an efficient documentation methods in lab which is practiced by scientists, could positively influence the chances for reproducibility. The main research question is Do semantic technologies and their applications contribute to the efficiency of the documentation task and improve the quality of experimental metadata provided by the laboratory researchers in the domain of Food Chemistry?” In this study, we first identify quality criteria in the domain for experimental documentation, starting with method descriptions found in literature. We evaluate in detail the methods reporting in a comprehensive set of laboratory experiments that should enable valid reproduction, integration and comparison of research procedures. In our work we focus on Food Chemistry, assuming that the outcomes will be valuable for other domains as well. Second, we define indicators to measure the efficiency of the documentation task as performed in the lab. We develop vocabularies to formally describe the domain knowledge. Finally, given the developed models and defined metrics we design a prototype tool that supposedly will assist researchers in efficiently producing high-quality lab notes. We will set up an intervention study to evaluate the tool and underlying hypothesis in the context of a Food Chemistry research group.
Semantic support for recording laboratory experimental metadata: a study in Food Chemistry
Negative or Inconclusive rEsults in Semantic web. Every Semantic Web researcher has been there: you spend days of work, but the results just don’t give the answer you were hoping for. The work ends up, like so many, part of the File Drawer Effect: they never get reported because of negative or inconclusive outcome. This occurs as a result of a publication bias towards positive results in Semantic Web, as in other fields. However, negative or inconclusive results are fundamental to the research process and can be as valuable as positive results. This workshop provides a forum for such attempted approaches, methodologies, or implementations. Researchers are urged to report null, disappointing or inconclusive attempts in the Semantic Web and Linked Open Data research field. We specifically target sound approaches and, scientifically and technically relevant contributions, that produced negative or inconclusive experimental results.
With the increasing application of Linked Open Data, assessing the quality of datasets by computing quality metrics becomes an issue of crucial importance. For large and evolving datasets, an exact, deterministic computation of the quality metrics is too time consuming or expensive. We employ probabilistic techniques such as Reservoir Sampling, Bloom Filters and Clustering Coefficient estimation for implementing a broad set of different data quality metrics in an approximate but sufficiently accurate way. Our implementation is integrated in the comprehensive data quality assessment framework Luzzu and evaluated in terms of performance and accuracy on Linked Open Datasets of broad relevance.
Quality Assessment of Linked Datasets using Probabilistic Approximations
FOODpedia: Russian food products as a Linked Data Dataset
Open and efficient sharing of information about food products and their ingredients is important for all parties of the chain ranging from the manufactures to consumers. There exist a public catalogue of some Russian food products (http://goodsmatrix.ru) that is used by some manufactures and consumers. Although the information is open, there are many difficulties in using the site, e.g., interoperability, querying and linking that could be mitigated by Semantic Web technologies. This paper presents an approach and a project for extracting and publishing information about food products and also linking it to existing datasets in Linked Open Data Cloud.
The ESWC 2015 PhD Symposium is a chance for PhD students working in all areas of Semantic Web research to present their work meet with peers and experienced researchers obtain feedback and learn from each others experiences. It aims at helping future researchers in building up the skills and confidence required to conduct and promote their research as well as providing them with an opportunity to attend one of the most important research conferences on the Semantic Web.
PhDSymposium full day event: PhDSymposium
Exploiting Semantics from Ontologies to Enhance Accuracy of Similarity Measures
Precisely determining similarity between entities becomes a building block for pattern discovery, and existing semantic similarity measures tackle this problem by considering ontological annotations of the entities to decide relatedness. Nevertheless, because semantic similarity measures only rely on the ontology topology and blindly treat explicit ontology facts, they may erroneously assign high values of similarity to dissimilar entities. We propose ColorSim a similarity measure that considers semantics of the annotations, e.g., relationship types, and implicit facts and their inferring processes, to accurately compute the relatedness of two annotated entities. We compare ColorSim with state-of-theart approaches and report on preliminary experimental results that suggest the benefits of exploiting knowledge encoded in the ontologies to measure similarity.
Searching for entities is a common user activity on the Web. There is an increasing effort in developing entity search techniques in the research community. Existing approaches are usually based on static measures that do not reflect the time-awareness, which is a factor that should be taken into account in entity search. In this paper, we propose a novel approach to time-aware entity search in DBpedia, that takes into account both popularity and temporality of entities. The experimental results show that our approach can significantly improve the performance of entity search with temporal focus compared with the baselines.
Challenges posters will be shown during afternoon coffee break.
A scalable adaptive method for complex reasoning over semantic data streams
Software Engineering for Business Information Systems - Technical University Munich
German Research Center for Artificial Intelligence (DFKI)
Background knowledge from Linked Open Data sources can be used to improve the results of a data mining problem at hand: predictive models can become more accurate, and descriptive models can reveal more interesting findings. However, collecting and integrating background knowledge is tedious manual work. In this paper we propose a set of desiderata, and identify the challenges for developing a framework for unsupervised generation of data mining features from Linked Data.
Towards Linked Open Data enabled Data Mining: Strategies for Feature Generation, Propositionalization, Selection, and Consolidation
Knowledge Media Institute, the Open University
Sequential Decision Making with Medical Interpretation Algorithms in the Semantic Web
Centre for Artificial Intelligence Research, UKZN and CSIR Meraka
This paper proposes an approach to gathering annotated semantic data, which rejects the notion that human interpretation can have a single ground truth, and is instead based on the observation that disagreement between annotators can signal ambiguity in the input text, as well as how the annotation task has been designed. The purpose of this research is to investigate whether disagreement-aware crowdsourcing is a scalable approach to gather semantic annotation across various tasks and domains. We propose a methodology for answering this question that involves, for each task and domain: defining the crowdsourcing setup, experimental data collection, and evaluating both the setup and the results. We present initial results for the task of medical relation extraction, and propose an evaluation plan for crowdsourcing semantic annotation for several tasks and domains.
Crowdsourcing Disagreement for Collecting Semantic Annotation
PhDSymposium paper PhDSymposium: Exploiting Semantics from Ontologies to Enhance Accuracy of Similarity Measures
Reseach paper LinkedData & Data Management: Heuristics for Fixing Common Errors in Deployed schema.org Microdata
Visual Analytics with Linked Open Data and Social Media (VisLOD). The increasing availability of essential information as Linked Open Data has empowered ordinary citizens and organisations with means to increase their understanding of their political, social, economic and cultural environment. Social Media, on the other hand, offer means to share experiences, connect with friends/relatives, keep updated with situations and understand the public perception of events, places or individuals. With the availability of large datasets, a considerable challenge is how to quickly and efficiently consume such datasets. Visual Analytics solutions offer a promising direction of research for consuming large semantic datasets and quickly identify hidden trends, patterns and anomalies, obtain overviews of large data spaces and promptly reach data points of interest. The goal of the tutorial is to bring together researchers and practitioners interested in visual and interactive techniques for exploring Social Media, Linked Open Data and Web Content. The hands-on tutorial will cover technical aspects from two perspectives: data collection and data analysis using Visualisation techniques. Data collection hand-on will include gathering (i) Social Media content, (ii) Linked Data and (iii) performing Information Extraction on Web content. The data analysis hands-on will introduce various open source visualisation tools and will cover design and implementation of web-based visual analytic solutions.
Linked Data Quality. Since the start of the Linked Open Data (LOD) Cloud, we have seen an unprecedented volume of structured data published on the web, in most cases as RDF and Linked (Open) Data. The integration across this LOD Cloud, however, is hampered by the ‘publish first, refine later’ philosophy. This is due to various quality problems existing in the published data such as incompleteness, inconsistency, incomprehensibility, etc. These problems affect every application domain, be it scientific (e.g., life science, environment), governmental, or industrial applications. We see linked datasets originating from crowdsourced content like Wikipedia and OpenStreetMap such as DBpedia and LinkedGeoData and also from highly curated sources e.g. from the library domain. Quality is defined as “fitness for use”, thus DBpedia currently can be appropriate for a simple end-user application but could never be used in the medical domain for treatment decisions. However, quality is a key to the success of the data web and a major barrier for further industry adoption. Despite the quality in Linked Data being an essential concept, few efforts are currently available to standardize how data quality tracking and assurance should be implemented. Particularly in Linked Data, ensuring data quality is a challenge as it involves a set of autonomously evolving data sources. Additionally, detecting the quality of datasets available and making the information explicit is yet another challenge. This includes the (semi-)automatic identification of problems. Moreover, none of the current approaches uses the assessment to ultimately improve the quality of the underlying dataset. The goal of the Workshop on Linked Data Quality is to raise the awareness of quality issues in Linked Data and to promote approaches to assess, monitor, maintain and improve Linked Data quality.
PhDSymposium paper PhDSymposium: Crowdsourcing Disagreement for Collecting Semantic Annotation
In-Use paper InUse & Industry 1: Crowdmapping Digital Social Innovation with Linked data
Cloud Computing has several provision models, e.g. Infrastructure as a service (IaaS). However, cloud users (tenants) have limited or no control over the underlying network resources. Network as a Service (NaaS) is emerging as a novel model to fill this gap. However, NaaS requires an approach capable of modeling the underlying network resources capabilities in abstracted and vendor-independent form. In this paper we elaborate on SemNaaS, a Semantic Web based approach for developing and supporting operations of NaaS systems. SemNaaS can can work with any NaaS provider. We integrated it with the existing OpenNaaS framework. We propose Network Markup Language (NML) as the ontology for describing networking infrastructures.Based on that ontology, we develop a network modeling system and integrate with OpenNaaS.Furthermore, we demonstrate the capabilities that Semantic Web can add to the NaaS paradigm by applying SemNaaS operations to a specific NaaS use case.
SemNaaS: Add a Semantic Dimension to the Network as a Service
Department of Computer Science, The University of Texas at Austin
Multilingual Semantic Web. The Multilingual Semantic Web workshop series is concerned with research questions on how current Semantic Web infrastructure can and should be extended to advance the Semantic Web and linked data use and development across language communities around the world. This raises several challenges such as ontology localisation, cross-lingual question answering, cross-lingual ontology and data matching, representation of lexical information on the Web of Data, etc. An infrastructure should be in place for defining ontologies and vocabularies in multiple languages with a transparent semantics across them. NLP and machine learning for linked data can benefit from exploiting multilingual language resources such as annotated corpora, WordNets, bilingual dictionaries, etc. if they are themselves formally represented and linked by following the linked data principles. We argue that a critical mass of language resources as linked data on the Web can lead to a new generation of linked data-aware NLP techniques and tools which, in turn, will serve as basis for richer multilingual multimedia Web content analysis. In addressing such research topics, the workshop aims at providing a forum for researchers at the intersection of NLP, multilingual information access, Linked Data and the Semantic Web to exchange ideas on realising the Multilingual Semantic Web.
Civil and Environmental Engineering - Penn State University
Reseach paper Reasoning: Large scale rule-based Reasoning using a Laptop
(35) Martin Peters, Sabine Sachweh and Albert Zündorf.
In this paper, we outline our ongoing work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large RDF graphs.
Towards Scalable Visual Exploration of Very Large RDF Graphs
School of ITEE, The University of Queensland
An increasing number of linked datasets is published on the Web, expressed in RDF(S)/OWL. Interlinking, matching or querying these datasets require some knowledge about the types and properties they contain. This work presents an approach, relying on a clustering algorithm, which provides the types describing a dataset.
Kno.e.sis Center, Wright State University
Linguistic Modelling Laboratory, IPP, Bulgarian Academy of Sciences
Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry
Named Entity Resolution (NER) is an information extraction task that involves detecting mentions of named entities within texts and mapping them to their corresponding entities in a given knowledge source. Systems and frameworks for performing NER have been developed both by the academia and the industry with different features and capabilities. Nevertheless, what all approaches have in common is that their satisfactory performance in a given scenario does not constitute a trustworthy predictor of their performance in a different one, the reason being the scenario's different parameters (target entities, input texts, domain knowledge etc.). With that in mind, we describe in this paper a metric-based Diagnostic Framework that can be used to identify the causes behind the low performance of NER systems in industrial settings and take appropriate actions to increase it.
Reseach paper Crowd Sourcing & Web Science: Towards hybrid NER: a study of content and crowdsourcing-related performance factors
(134) Oluwaseyi Feyisetan, Markus Luczak-Roesch, Elena Simperl and Ramine Tinati.
Tutorial half day tutorial: Soc Med Gate
Practical Annotation and Processing of Social Media with GATE. Social media is fast becoming a crucial part of our everyday lives, not only as a fun and practical way to share our interests and activities with geographically distributed networks of friends, but as an important part of our business lives also. Processing social media is particularly problematic for automated tools, because it is a strong departure from the tradition of newswire that many tools were developed with and evaluated against, and also due to the terse and low-context language it typically comprises. This tutorial will address these issues in the context of the semantic web by introducing some of the problems faced by using NLP tools on social media, and solutions to these problems, including those specifically implemented in GATE and recently made publicly available. It will demonstrate techniques for extracting the relevant information from unstructured text in social media, so that participants will be equipped with the necessary building blocks of knowledge to build their own tools and tackle complex issues. Since all of the NLP tools to be presented are open source, the tutorial will provide the attendees with skills which are easy to apply and do not require special software or licenses.
The Web was originally developed to support collaboration in science. Although scientists benefit from many forms of collaboration on the Web (e.g., blogs, wikis, forums, code sharing, etc.), most collaborative projects are coordinated over email, phone calls, and in-person meetings. Our goal is to develop a collaborative infrastructure for scientists to work on complex science questions that require multi-disciplinary contributions to gather and analyze data, that cannot occur without significant coordination to synthesize findings, and that grow organically to accommodate new contributors as needed as the work evolves over time. Our approach is to develop an organic data science framework based on a task-centered organization of the collaboration, includes principles from social sciences for successful on-line communities, and exposes an open science process. Our approach is implemented as an extension of a semantic wiki platform, and captures formal representations of task decomposition structures, relations between tasks and users, and other properties of tasks, data, and other relevant science objects. All these entities are captured through the semantic wiki user interface, represented as semantic web objects, and exported as linked data.
Supporting Open Collaboration in Science through Explicit and Linked Semantic Description of Processes
(119) Pierre-Edouard Portier, Mazen Alsarem, Sylvie Calabretto and Harald Kosch.
Reseach paper Crowd Sourcing & Web Science: Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets
École nationale supérieure des mines de Saint-Étienne
Invited Talk: Massimo Poesio - What Crowdsourcing Tells Us about Cognition: the Case of Anaphora
Crowdsourcing is usually seen primarily as an inexpensive and quick way of creating large resources for a variety of Artificial Intelligence tasks. However, our work with Phrase Detectives, a game-with-a-purpose designed to collect data about anaphora, suggests that collecting large numbers of judgments about very large amounts of data also tells us a lot about the extent to which human subjects agree or disagree about the interpretation of such data. In the talk I will introduce Phrase Detectives and discuss our results and their implications.
A major challenge in information management today is the integration of huge amounts of data distributed across multiple data sources. One suggested approach to this problem is ontology-based data integration where legacy data systems are integrated via a common on- tology that represents a unified global view over all data sources. In many domains (e.g., biology, medicine) there exist established ontolo- gies to integrate data from existing data sources. However, data is often not natively born using these ontologies. Instead, much data resides in relational databases. Therefore, mappings that relate the legacy data sources to the ontology need to be constructed. Recent techniques and systems that automatically construct such mappings have been devel- oped. The quality metrics of these systems are, however, often only based on self-designed, highly biased benchmarks. This paper introduces a new publicly available benchmarking suite called RODI which is designed to cover a wide range of integration challenges in Relational-to-Ontology Data Integration scenarios. RODI provides a set of different relational data sources and ontologies as well as a scoring function with which the performance of relational-to-ontology mapping construction systems may be evaluated.
RODI: A Benchmark for Relational-to-Ontology Data Integration
University Panthéon - Sorbonne (Paris I)
Dipartimento di Informatica, Università di Bari
Invited Talk: Viktor Mayer-Schönberger - Why Big Data Matters - a lot
Much has been made of "big data", our ability to gain novel insights from a comprehensive set of data points, but a lot of it is hype, and marketing-speak to sell more tools and consulting. In this talk, I will explain what Big Data really is, why it isn¹t just a marketing fad or the tool du jour, but a new way of making sense of the world around us, and consequently why Big Data matters a great deal, in particular also in the context of semantic technologies. But I will also mention why we need to be cautious and well aware of Big Data limitations when utilizing it.
Universidad Simon Bolivar, Dept. Computer Science
A Compact In-Memory Dictionary for RDF data
We present a method for a compact in-memory RDF dictionary that supports high frequency updates. Our method leverages the long common prefixes in RDF terms to compress them using a trie data structure. To overcome the memory inefficiency of tries, we present a highly memory efficient implementation which is especially tuned for RDF data. Our approach compacts the dictionary further by unifying the two independent tables that are normally used for encoding and decoding into a single table by mapping each string to a memory location from where it can be reached again instead of counter ID. To motivate our approach, we present empirical data on the amount of repetition that is present in both IRIs and literals of large realistic RDF graphs. An empirical analysis shows that our technique saves 50-59% memory compared to uncompressed conventional dictionaries while still offering comparable encoding/decoding performance.
Social: Fourth Bus for Piran Salt Pans Visit (German tour guide)
National Technical University of Athens (NTUA)
PhDSymposium paper PhDSymposium: A scalable adaptive method for complex reasoning over semantic data streams
Invited Talk: Lise Getoor - Combining Statistics and Semantics to Turn Data into Knowledge
Addressing inherent uncertainty and exploiting structure are fundamental to turning data into knowledge. Statistical relational learning (SRL) builds on principles from probability theory and statistics to address uncertainty while incorporating tools from logic to represent structure. In this talk I will overview our recent work on probabilistic soft logic (PSL), an SRL framework for collective, probabilistic reasoning in relational domains. PSL is able to reason holistically about both entity attributes and relationships among the entities, along with ontological constraints. The underlying mathematical framework supports extremely efficient inference. Our recent results show that by building on state-of-the-art optimization methods in a distributed implementation, we can solve large-scale knowledge graph extraction problems with millions of random variables orders of magnitude faster than existing approaches.
Multimedia Lab – iMinds – Ghent University
University of Sheffield, Department of Computer Science
Social: Third Bus for Piran Salt Pans Visit (English tour guide)
(26) Ruslan Mavlyutov, Marcin Wylot and Philippe Cudré-Mauroux.
Reseach paper LinkedData & Data Management: A Comparison of Data Structures to Manage URIs on the Web of Data
The decentral architecture behind the Web has led to pieces of information being distributed across data sources with varying structure. Hence, answering complex questions often required combining information from structured and unstructured data sources. We present HAWK, a novel entity search approach for Hybrid Question Answering based on combining Linked Data and textual data. The approach uses predicate-argument representations of questions to derive equivalent combinations of SPARQL query fragments and text queries. These are executed so as to integrate the results of the text queries into SPARQL and thus generate a formal interpretation of the query. We present a thorough evaluation of the framework, including an analysis of the influence of entity annotation tools on the generation process of the hybrid queries and a study of the overall accuracy of the system. Our results show that HAWK 0.68 respectively 0.61 F-measure within the training respectively test phases on the Question Answering over Linked Data (QALD-4) hybrid query benchmark.
HAWK - Hybrid Question Answering using Linked Data
Information Sciences Institute - University of Southern California
Break: Coffee Break + Challenges Posters
(32) José M. Giménez-Garcia, Javier D. Fernández and Miguel A. Martinez-Prieto.
Reseach paper Semantic Data Management & Big Data 2: HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce
QueryVOWL: Visual Composition of SPARQL Queries
In order to make SPARQL queries more accessible to users, we have developed the visual query language QueryVOWL. It defines SPARQL mappings for graphical elements of the ontology visualization VOWL. In this demo, we present a web-based prototype that supports the creation, modification, and evaluation of QueryVOWL graphs. Based on the selected SPARQL endpoint, it provides suggestions for extending the query, and retrieves IRIs and literals according to the selections in the QueryVOWL graph. In contrast to related work, SPARQL queries can be created entirely with visual elements.
Dataset PROFIling and fEderated Search for Linked Data. The PROFILES’15 workshop is a continuation of the workshop series successfully started as PROFILES’14 @ ESWC 2014. These workshops aims to gather innovative query and search approaches for large-scale, distributed and heterogeneous linked datasets inline with dedicated approaches to analyse, describe and discover endpoints, as an inherent task of query distribution and dataset recommendation. The PROFILES’15 workshop aims to become a highly interactive research forum for researchers. PROFILES’15 will bring together researchers and practitioners in the fields of Semantic Web and Linked Data, Databases, Semantic Search, Text Mining, NLP as well as Information Retrieval. PROFILES’15 will gather novel works from the fields of semantic query interpretation and federated search for Linked Data, dataset selection and discovery as well as automated profiling of datasets using scalable data assessment and profiling techniques. PROFILES’15 will equally consider both novel scientific methods and techniques for querying, assessment, profiling, discovery of distributed datasets as well as the application perspective, such as the innovative use of tools and methods for providing structured knowledge about distributed datasets, their evolution and fundamentally, means to search and query the Web of Data. We will seek application-oriented, as well as more theoretical papers and position papers.
Department of Informatics, University of Oslo
Social: First Bus for Piran Salt Pans Visit (English tour guide)
VocBench: a Web Application for Collaborative Development of Multilingual Thesauri
In this paper, we present VocBench, an open source web application for editing thesauri complying with the SKOS and SKOS-XL standards. VocBench has a strong focus on collaboration, supported by workflow management for content validation and publication. Dedicated user roles provide a clean separation of competences, addressing different specificities ranging from management aspects to vertical competences on content editing, such as conceptualization versus terminology editing. Extensive support for scheme management allows editors to fully exploit the possibilities of the SKOS model, as well as to fulfill its integrity constraints. We discuss thoroughly the main features of VocBench, detail its architecture, and then evaluate it under both a functional and user-appreciation ground, through a comparison with other similar tools and by performing an analysis of user questionnaires, respectively. Finally, we provide insights on future developments.
Summarizing and Presenting Entities and Ontologies - Human Semantic Web Interaction. The Open Data and Semantic Web efforts have been promoting and facilitating the publication and integration of data from diverse sources, giving rise to a large and increasing volume of machine-readable data available on the Web. Even though such raw data and its ontological schema enable the interoperability among Web applications, problems arise when exposing them to human users, as to how to present such large-scale, structured data in a user-friendly manner. To meet the challenge, we invite research contributions on all aspects of ranking, summarization, visualization, and exploration of entities, ontologies, knowledge bases and Web Data, with a particular focus on their summarization and presentation. We also welcome submissions on novel applications of these techniques. The workshop is expected to be a forum that brings together researchers and practitioners from both academia and industry in the areas of Semantic Web, information retrieval, data engineering, and human-computer interaction, to discuss high-quality research and emerging applications, to exchange ideas and experience, and to identify new opportunities for collaboration.
Reseach paper Natural Language Processing: HAWK - Hybrid Question Answering using Linked Data
(144) Ricardo Usbeck, Axel-Cyrille Ngonga Ngomo, Lorenz Bühmann and Christina Unger.
Reseach paper Reasoning: RDF Digest: Efficient Summarization of RDF/S KBs
Ad-hoc querying is crucial to access information from Linked Data, yet publishing queryable RDF datasets on the web is not a trivial exercise. The most compelling argument to support this claim is that the Web contains hundreds of thousands of data documents, while only 300 SPARQL endpoints are provided. Even worse, the SPARQL endpoints we do have are often unstable, may not comply with the standards, and may differ in supported features. In other words, hosting data online is easy, but publishing Linked Data via a queryable API such as SPARQL appears to be too difficult. As a consequence, in practice, there is no single uniform way to query the LOD cloud today. In this paper, we therefore combine a large-scale Linked Data publication project (LOD Laundromat) with a low-cost server-side interface (Triple Pattern Frag- ments), in order to bridge the gap between the web of downloadable data documents and the web of live queryable data. The result is a repeat- able, low-cost, open-source-based data publication process. To demon- strate its applicability, we made over 550.000 data documents available as data APIs, consisting of over 21 billion triples.
Data as a Service: The Semantic Web Redeployed
Institute for the Management of Information Systems
Tutorial half day tutorial: Comp Soc Sci
Computational Social Science For the Semantic Web. This tutorial covers the basics of computational social sciences from a computer science perspective, and its application to Semantic Web research. The tutors will teach the basics of applying theory, methods and techniques across various scenarios. We will cover the application of theory as a means to interrogate the start-to-end process of experimentation, and teach how to work in the qualitative and quantitative paradigm, and how to add real-world meaning to experiments.
DaCENA: Serendipitous News Reading with Data Contexts
DaCENA (Data Context for News Articles) is a web application that showcases a new approach to reading online news articles with the support of a data context built from interlinked facts available in the Web of Data. Given a source article, a set of facts that are estimated to be more interesting for the readers are extracted from the Web and presented using tailored information visualization methods and an interactive user interface. By looking at these facts, the reader access background factual knowledge with the advantage of being supported in the interpretation of the news content and being suggested connections to related topics to further explore.
A survey of HTTP caching implementations on the open Semantic Web
Scalability of the data access architecture in the Semantic Web is dependent on the establishment of caching mechanisms to take the load off of servers. Unfortunately, there is a chicken and egg problem here: Research, implementation, and evaluation of caching infrastructure is uninteresting as long as data providers do not publish relevant metadata. And publishing metadata is useless as long as there is no infrastructure that uses it. We show by means of a survey of live RDF data sources that caching metadata is prevalent enough already to be used in some cases. On the other hand, they are not commonly used even on relatively static data, and when they are given, they are very conservatively set. We point out future directions and give recommendations for the enhanced use of caching in the Semantic Web.
European Commission - Joint Research Centre
Semi-supervised Instance Matching Using Boosted Classifiers
Instance matching concerns identifying pairs of instances that refer to the same underlying entity. Current state-of-the-art instance matchers use machine learning methods. Supervised learning systems achieve good performance by training on significant amounts of manually labeled samples. To alleviate the labeling effort, this paper presents a minimally supervised instance matching approach that is able to deliver competitive performance using only 2% training data and little parameter tuning. As a first step, the classifier is trained in an ensemble setting using boosting. Iterative semi-supervised learning is used to improve the performance of the boosted classifier even further, by re-training it on the most confident samples labeled in the current iteration. Empirical evaluations on a suite of six publicly available benchmarks show that the proposed system outcompetes optimization-based minimally supervised approaches in 1-7 iterations. The system's average F-Measure is shown to be within 2.5% of that of recent supervised systems that require more training samples for effective performance.
Reseach Track Cognition & WebScience: Cognition & WebScience
Indented tree has been widely used to organize information and visualize graph-structured data like RDF graphs. Given a starting resource in a cyclic RDF graph, there are different ways of transforming the graph into a tree representation to be visualized as an indented tree. It would be interesting to investigate whether and how these different representations influence the user's browsing experience. In this paper, we address this issue from the coherence aspect of tree representation. We aim to smooth the user's reading experience by visualizing an optimal indented tree in the sense of featuring the fewest reversed edges, which often cause confusion and interrupt the user's cognitive process due to lack of a general, effective way of presentation. To achieve this, we propose a two-step approach that is theoretically proved to generate such an optimal tree representation for a given RDF graph and a specified starting resource. We also empirically show the difference in coherence between tree representations of real-world RDF graphs generated by our approach and two baseline approaches that have been widely adopted. These different tree representations lead to significantly different user experience in our preliminary user study, which reports a considerable degree of dependence between coherence and user experience.
On Coherent Indented Tree Visualization of RDF Graphs
While much work has focused on continuously publishing Linked Open Data, little work considers how to help consumers to better understand existing datasets. ABSTAT framework aims at providing a better understanding of big and complex datasets by extracting summaries of linked data sets based on an ontology-driven data abstraction model. Our ABSTAT framework takes as input a data set and an ontology and returns an ontology-driven data summary as output. The summary is exported into RDF and then made accessible through a SPARQL endpoint and a web interface to support the navigation.
ASBTAT: Linked Data Summaries with ABstraction and STATistics
Univ. Grenoble Alpes \& Inria Grenoble Rhône-Alpes
Institute for Web Science and Technologies (WeST), University of Koblenz - Landau
Large-scale knowledge graphs such as those in the Linked Data cloud are typically represented as subject-predicate-object triples. However, many facts about the world involve more than two entities. While n-ary relations can be converted to triples in a number of ways, unfortunately, the structurally different choices made in different knowledge sources significantly impede our ability to connect them. They also make it impossible to query the data concisely and without prior knowledge of each individual source. We present FrameBase, a wide-coverage knowledge-base schema that uses linguistic frames to seamlessly represent and query n-ary relations from other knowledge bases, at different levels of granularity connected by logical entailment. It also opens possibilities to draw on natural language processing techniques for querying and data mining.
FrameBase: Representing N-ary Relations using Semantic Frames
Department of Computer Science, Tsinghua University
Rubya: a tool for generating rules for incremental maintenance of RDF views
We present Rubya, a tool that automatically generates the RDF view defined on top of relational data and all rules required for the incremental maintenance of the RDF view. Our approach relies on the designer to specify a mapping between the relational schema and a target ontology and results in a specification of how to represent relational schema concepts in terms of RDF classes and properties of the designers choice. Based on this mapping the rules for incrementally maintenance of the RDF view is generated.
Assigning Semantic Labels to Data Sources
There is a huge demand to be able to integrate heterogeneous data sources, which requires mapping the attributes of a source to the concepts and relationships defined in a domain ontology. In this paper, we present a new approach to the problem of identifying these mappings, which we call semantic labeling. Previous approaches map each data value individually, typically by learning a model based on features extracted from the data using supervised machine learning techniques. Our approach differs from existing approaches in that we take a holistic view of the data values corresponding to a semantic label and use techniques that treat this data collectively, which makes it possible to capture characteristic properties of the values associated with a semantic label as a whole. Our approach supports both textual and numeric data and proposes the top-k semantic labels along with their associated confidence scores. Our experiments show that the approach has higher label prediction accuracy, has lower time complexity, and is more scalable than existing systems.
Publishing and Interlinking Linked Geospatial Data. In this tutorial we present the life cycle of linked geospatial data and we focus on two important steps: the publication of geospatial data as RDF graphs and the discovery of spatial and temporal links between linked geospatial data. Given the proliferation of geospatial information on the Web many kinds of geospatial data are now becoming available as linked datasets (e.g., Google and Bing maps, user-generated geospatial content etc.). The topic of the tutorial is related to all core research areas of the Semantic Web (e.g., semantic information extraction, transformation of data into RDF graphs, interlinking linked data etc.) since there is often a need to re-consider existing core techniques when we deal with geospatial information. Thus, it is timely to train Semantic Web researchers, especially the ones that are in the early stages of their careers, on the state of the art of this area and invite them to contribute to it. In this tutorial we give a comprehensive background on data models, query languages, implemented systems for linked geospatial data, we discuss recent approaches on publishing geospatial data as RDF graphs and we discuss techniques for interlinking linked data by taking into account their spatial and temporal extent. The tutorial is complemented with a hands on session that will familiarize the audience with the state-of-the-art tools in publishing and interlinking geospatial information.
PhDSymposium PhDSymposium: Testimonial: Enrico Motta
VocBench is an open source web application for editing of SKOS and SKOS-XL thesauri, with a strong focus on collaboration, supported by workflow manage-ment for content validation and publication. Dedicated user roles provide a clean separation of competences, addressing different specificities ranging from man-agement aspects to vertical competences on content editing, such as conceptual-ization versus terminology editing. Extensive support for scheme management al-lows editors to fully exploit the possibilities of the SKOS model, as well as to fulfill its integrity constraints. We describe here the main features of VocBench which will be shown along the demo. Finally, we provide insights on future developments.
Collaborative Development of Multilingual Thesauri with VocBench (System Description and Demonstrator)
The Open University - Knowledge Media Institute
Detection, Representation, and Exploitation of Events in the Semantic Web. n recent years, researchers in several communities involved in aspects of information science have begun to realise the potential benefits of assigning an important role to events in the representation and organisation of knowledge and media-benefits which can be compared to those of representing entities such as persons or locations instead of just dealing with more superficial objects such as proper names and geographical coordinates. While a good deal of relevant research for example, on the modeling of events has been done in the semantic web community, much complementary research has been done in other, partially overlapping communities, such as those involved in multimedia processing, information extraction, sensor processing and information retrieval research. However, these areas often deal with events with a different perspective. The attendance at previous DERIVE workshops proved that there is a great interest from many different communities in the role of events. The results presented in there also indicated that dealing with events is still an emerging topic. The goal of this workshop is to advance research on the role of events within the information extraction and semantic web communities, both building on existing work and integrating results and methods from other areas, while focusing on issues of special importance for the semantic web.
SmartKeepers: a decentralized, secure, and flexible social platform for coworkers
Coworking has emerged as an attractive model for organization. It relies on a highly dynamic collaborations among different partners. Traditional centralized social platform lack fundamental requirements for such collaborations potentially short, that are the management of the dynamic topology of such professional networks, privacy, data exchanges and ownership. In this paper, we present SmartKeepers, a decentralized and secure environment for coworking activity. Each user physically owns her node that she plugs in and out as she moves from one collaborative space to another. The system supports a large variety of network topologies and is fully interoperable with W3C compliant solutions. We showcase the cogency of the SemanticWeb for building decentralized and secure services while keeping every user at the core of the data ownership process.
Legal Domain And Semantic Web Applications. The two research areas of Legal Knowledge and Information Systems, and the Semantic Web are very much interconnected. The legal domain is an ideal field of study for Semantic Web researchers, as it uses and contributes to most of the topics that are relevant to the community. Given the complex interactions of legal actors, legal sources and legal processes, as well as the relevance and potential impact of decisions in the juridical and social processes of a country, it provides a challenging context and an important opportunity for groundbreaking research results. At the same time, Semantic Web formalisms and technologies provide a set of technical instruments which can be fruitfully adopted by legal experts to represent, interlink, and reason over legal knowledge and related aspects such as provenance, privacy, and trust. In particular, Semantic Web technology facilitates standards-based legal knowledge representation, which enables the possibility of legal information reuse over the Web. Ontologies, knowledge extraction and reasoning techniques have been studied by the Artificial Intelligence & Law community for years, but only few and sparse connections with the Semantic Web community have resulted from these interactions. The aim of this workshop is to study the challenges that the legal domain poses to Semantic Web research, and how Semantic Web technologies and formalisms can contribute to address these open issues. This way, we promote the use of legal knowledge for addressing Semantic Web research questions and, vice versa, to use Semantic Web technologies as tools for reasoning over legal knowledge.
Sépage & STIH, Université Paris-Sorbonne
Social: Second Bus for Piran Salt Pans Visit (English tour guide)
(56) Dmitry Muromtsev, Peter Haase, Dmitry Pavlov, Eugene Cherny, Alexey Andreev and Anna Spiridonova.
In-Use paper InUse & Industry 3: Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing
entity disambiguation using Linked Open Data
Semantic annotation exploiting linked data
LOD-based Disambiguation of Named Entities in @tweets through Context #enrichment
Social media -- and microblogs in particular -- have emerged as high-value, high-volume content, which organisations increasingly wish to analyse automatically. These short, noisy, context-dependent, stylized and dynamic texts currently present a significant challenge for semantic annotation techniques, such as ontology-based named entity disambiguation. State-of-the-art systems, however, have largely ignored the richer, microblog-specific context, which humans draw upon when interpreting these short messages. This paper focuses specifically on quantifying the impact on entity disambiguation performance when readily available contextual information is included from URL content, hash tag definitions, and Twitter user profiles. In particular, including URL content significant improves the performance of a state-of-the-art DBpedia-based entity linking system. Similarly, user profile information for @mentions improves recall by over 10% with no adverse impact on precision. Another contribution lies in a new, publicly available corpus of tweets, which have been hand-annotated with DBpedia URIs, with high inter-annotator agreement.
Information Extraction for Social Media Mining
Insight - National University of Ireland
Ontology and Conceptual Modeling Research Group (NEMO)/Federal University of Espirito Santo (UFES)
Center for Integrated Data Analytics - U.S. Geological Survey
Istituto di Linguistica Computazionale - CNR
There is an increasing amount of (semi)-structured data found on the Web that exhibits a graph data model. Although it provides comprehensive details about individual entities, the entities themselves are not the item of interest, but rather an aggregated view of the data. The analysis of graph data is a common task, thus, there is a need for an analytical framework that facilitates this. In this work, we propose a graph aggregation operator, called Gagg, that is flexible with regards to the data’s structure and analysis requirements. We formalise the semantics of Gagg so it can be part of a wider graph processing algebra. We believe such an operator is core to such an algebra, where several optimisations are possible. We evaluate Gagg over the BSBM and SP2B benchmarks and we find that it improves the graph aggregation performance by a factor of three.
RDF Digest: Efficient Summarization of RDF/S KBs
The exponential growth of the web and the extended use of semantic web tech-nologies has brought to the fore the need for quick understanding, flexible explo-ration and selection of complex web documents and schemas. To this direction ontology summarization aspires to produce an abridged version of the original ontology that highlights the most representative concepts of it. In this paper, we present RDF Digest, a novel platform that automatically produces summaries of RDF/S Knowledge Bases. In our work, a summary is a valid RDF/S document that includes the most representative concepts of the schema adapted to the corre-sponding data instances. To construct this graph our algorithm exploits the se-mantics and the structure of the schema and the distribution of the corresponding data/instances as well. The performed evaluation demonstrates the benefits of our approach and the considerable advantages gained.
LIDSEARCH: A SPARQL-driven Framework for searching Linked Data and Semantic Web Services
The amount of data in the Linked Open Data cloud is massively growing but there are a lot of missing, outdated and incomplete data that can be compensated by invoking web services. We introduce LIDSEARCH, a SPARQL-driven for searching linked data and relevant semantic web services with a single user query. Our framework extends the linked data search with a service search by rewriting the data query into multiple service requests in SPARQL for different service description languages. In this demo paper we validate the feasibility of our approach with a prototype as a proof of concept.
A Comparison of Data Structures to Manage URIs on the Web of Data
Uniform Resource Identifiers (URIs) are one of the corner stones of the Web; They are also exceedingly important on the Web of data, since RDF graphs and Linked Data both heavily rely on URIs to uniquely identify and connect online entities. Due to their hierarchical structure and their string serialization, sets of related URIs typically contain a high degree of redundant information and are systematically dictionary-compressed or encoded at the back-end (e.g., in the triple store). The paper represents, to the best of our knowledge, the first systematic comparison of the most common data structures used to encode URI data. We evaluate a series of data structures in term of their read/write performance and memory consumption.
PhDSymposium PhDSymposium: PhD Symposium Welcome
Ontology Engineering Group. Laboratorio de Inteligencia Artificial. Facultad de Informática. Universidad Politécnica de Madrid.
The OntoLex W3C Community Group has been working for more than three years on a shared lexicon model for ontologies. Alongside the core specification, the group also developed additional modules for specific tasks and use cases. In many usage scenarios the discovery and exploitation of linguistically grounded ontologies may benefit from summarizing information about their linguistic expressivity. That situation is compound by the fact that OntoLex allows the independent publication of ontologies, lexica and lexicalizations linking them. While the VoID vocabulary already addressed the need for general metadata about interlinked datasets, it is unable by itself to represent the more specific metadata relevant to OntoLex. To solve this problem, we developed a module of OntoLex, named LIME (Linguistic Metadata), which extends VoID with a vocabulary of metadata about the ontology-lexicon interface.
PhDSymposium paper PhDSymposium: Towards Linked Open Data enabled Data Mining: Strategies for Feature Generation, Propositionalization, Selection, and Consolidation
Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets
The advances of the Linked Open Data (LOD) initiative are giving rise to a more structured Web of data. Indeed, a few datasets act as hubs (e.g., DBpedia) connecting many other datasets. They also made possible new Web services for entity detection inside plain text (e.g., DBpedia Spotlight), thus allowing for new applications that will benefit from a combination of the Web of documents and the Web of data. To ease the emergence of these new use-cases, we propose a query-biased algorithm for the ranking of entities detected inside a Web page. Our algorithm combine link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset on which we compare our algorithm to the state of the art. Finally, we use this algorithm for the construction of semantic snippets for which we evaluate the usability and the usefulness with a crowdsourcing-based approach.
We present a novel approach, called SPSC, for the efficient composition of semantic services in unstructured peer-to-peer (P2P) networks. With SPSC, the peers are jointly planning complex IOPE-chained workflows of services in OWL-S together with their respective signature variable bindings in order to answer a given composition request. For this purpose, each peer exploits its local observation-based knowledge about the semantic overlay to contribute to the actual semantic service workflow, as well as to support the further exploration of alternatives in a heuristically pruned search space within the given TTL. In particular, the local query routing decisions are based on the application of two strategies for a guided composition plan branching and memorization of potentially helpful services. We show that the composition planning process with SPSC is sound and provide a lower bound of its completeness in terms of total recall of solutions for a given composition request, if they exist in the network. Our experimental evaluation revealed that SPSC can achieve a high cumulative recall with relatively low traffic overhead.
SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks
Ontop is an Ontology Based Data Access system allowing users to access a relational database through a conceptual layer provided by an ontology. In this demo, we use the recently developed NPD benchmark (+ 4 billion triples) to demonstrate the features of Ontop. First we use the Ontop as a SPARQL end-point to load the ontology and mappings, and answer SPARQL queries. Then, we will show how to use Ontop to check inconsistencies and exploit SWRL ontologies.
How to Stay Ontop of Your Data: Databases, Ontologies and More
Reseach paper Mobile Sensors, Services & Web of Things: Standardized and Efficient RDF Encoding for Constrained Embedded Networks
(63) Sebastian Käbisch, Daniel Peintner and Darko Anicic.
Reseach paper Semantic Data Management & Big Data 1: Processing Aggregate Queries in a Federation of SPARQL Endpoints
(28) Dilshod Ibragimov, Katja Hose, Torben Bach Pedersen and Esteban Zimanyi.
More and more RDF data is exposed on the Web via SPARQL endpoints. With the recent SPARQL 1.1 standard, these datasets can be queried in novel and more powerful ways, e.g., complex analysis tasks involving grouping and aggregation, and even data from multiple SPARQL endpoints, can now be formulated in a single query. This enables Business Intelligence applications that access data from federated web sources and can combine it with local data. However, as both aggregate and federated queries have become available only recently, state-of-the-art systems lack sophisticated optimization techniques that facilitate efficient execution of such queries over large datasets. To overcome these shortcomings, we propose a set of query processing strategies and the associated Cost-based Optimizer for Distributed Aggregate queries (CoDA) for executing aggregate SPARQL queries over federations of SPARQL endpoints. Our comprehensive experiments show that CoDA significantly improves performance over current state-of-the-art systems.
Processing Aggregate Queries in a Federation of SPARQL Endpoints
Institute for Information Business Vienna, University of Economics and Business
(164) Yolanda Gil, Felix Michel, Varun Ratnakar, Paul Hanson, Hilary Dugan, Jordan Read, Christopher Duffy and Matheus Hauder.
In-Use paper InUse & Industry 1: Supporting Open Collaboration in Science through Explicit and Linked Semantic Description of Processes
Reseach Track Ontology Matching & Summarization: Ontology Matching & Summarization
Institute AIFB, Karlsruhe Institute of Technology
PhDSymposium paper PhDSymposium: Sequential Decision Making with Medical Interpretation Algorithms in the Semantic Web
Reseach paper Semantic Data Management & Big Data 1: Intelligent and Efficient Techniques for RDF Query Relaxation
(156) Géraud Fokou, Stephane Jean and Allel Hadjali.
Department of Computer Science \& Engineering
Reseach paper Ontologies: Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning
In-Use Track InUse & Industry 2: InUse & Industry 2
Kiel University and Leibniz Information Center for Economics
Reseach paper Ontologies: VocBench: a Web Application for Collaborative Development of Multilingual Thesauri
(146) Armando Stellato, Sachit Rajbhandari, Andrea Turbati, Manuel Fiorelli, Caterina Caracciolo, Johannes Keizer and Maria Teresa Pazienza.
Algorithmic storytelling over Linked Data on the Web is a challenging task in which many graph-based pathfinding approaches experience issues with consistency regarding the resulting path that leads to a story. In order to mitigate arbitrariness and increase consistency, we propose to improve the semantic relatedness of concepts mentioned in a story by increasing the relevance of links between nodes through additional domain delineation and refinement steps. On top of this, we propose the implementation of an optimized algorithm controlling the pathfinding process to obtain more homogeneous search domain and retrieve more links between adjacent hops in each path. Preliminary results indicate the potential of the proposal.
Improving Semantic Relatedness in Paths for Storytelling with Linked Data on the Web
Reseach Track Natural Language Processing: Natural Language Processing
University of Applied Science Northweastern Switzerland
Reseach Track Semantic Data Management & Big Data 2: Semantic Data Management & Big Data 2
(75) Chun Lu, Milan Stankovic and Philippe Laublet.
In-Use paper InUse & Industry 3: Desperately searching for travel offers? Formulate better queries with some help from Linked Data.
A Decision Support System (DSS) in tunnelling domain deals with identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key area in diagnosing pathologies is to identify regions of the spread of pathologies. In practice, tunnel experts intuitively abstract such regions of interest and in doing so select tunnel portions that are susceptible to the same types of pathologies with some distance approximation. This complex diagnosis process is often subjective and poorly scales across cases and transport structures. In this paper, we introduce PADTUN system, a working prototype of DSS in tunnelling domain using semantic technologies. Ontologies are developed and used to capture tacit knowledge from tunnel experts. Tunnel inspection data are annotated with ontologies to take advantage of inferring capabilities offered by semantic technologies. In addition, an intelligent mechanism is developed to exploit abstraction and inference capabilities to identify regions of interest (ROI). PADTUN is developed in real-world settings offered by the NeTTUN EU Project and is applied in a tunnel diagnosis use case with Société Nationale des Chemins de Fer Français (SNCF), France. We show how the use of semantic technologies allows addressing the complex issues of pathology and ROI inferencing and matching experts’ expectations of decision support.
PADTUN - Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain
tunnel maintenance and diagnosis ontology
This ‘paper’, when viewed on the Web, is the demo itself, since the interactive and semantic features can be directly observed while reading/consuming. The demo showcases, how scholarly communication can adapt to the audience, whether the content is read on a screen or printed on paper, listen with a screen reader, watched as a movie, shown as a presentation, or even interacted with in the document. To experience the described features please open this document in your browser under its canonical URI: http://csarven.ca/this-paper-is-a-demo.
Computer and Information Science Department
A wealth of biomedical datasets is meanwhile published as Linked Open Data. Each of these datasets has a particular focus, such as providing information on diseases or symptoms of a certain kind. Hence, a comprehensive view can only be provided by integrating information from various datasets. Although, links between diseases and symptoms can be found, these links are far too sparse to enable practical applications such as a disease-centric access to clinical reports that are annotated with symptom information. For this purpose, we build a model of disease-symptom relations. Utilizing existing ontology mappings, we propagate semantic type information disease and symptom across ontologies. Then entities of the same semantic type from different ontologies are clustered and object properties between entities are mapped to cluster-level relations. The effectiveness of our approach is demonstrated by integrating all available disease-symptom relations from different biomedical ontologies resulting in a significantly increased linkage between datasets.
From Symptoms to Diseases -- Creating the Missing Link
Analysis of companies’ non-financial disclosures: Ontology learning by topic modeling
Prior studies highlight the merits of integrating Linked Data to aid investors’ analyses of company financial disclosures. Non-financial disclosures, including reporting on a company’s environmental footprint (corporate sustainability), remains an unexplored area of research. One reason cited by investors is the need for domain knowledge from earth science to interpret such disclosures. To address this challenge, we employ a fully-automated system which employs a Latent Dirichlet Allocation (LDA) model for the discovery of earth science topics in corporate sustainability text. The LDA model is seeded with a vocabulary generated by terms retrieved via a SPARQL endpoint. The terms are seeded as lexical priors into the LDA model. An ensemble tree combines the resulting topic probabilities and classifies the quality of sustainability disclosures using domain expert ratings published by Google Finance. From an applications stance, our results may be of interest to investors seeking to incorporate sustainability considerations into their investment decisions.
Reseach paper Machine Learning: Inductive Classification through Evidence-based Models and their Ensembles
(184) Giuseppe Rizzo, Claudia D'Amato, Nicola Fanizzi and Floriana Esposito.
University Hospital Erlangen, Department of Radiology
In-Use paper InUse & Industry 3: PADTUN - Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain
(19) Dhavalkumar Thakker, Vania Dimitrova, Anthony Cohn and Joaquin Valdes.
Pundit 2.0 is a semantic web annotation system that supports users in creating structured data on top of web pages. Annotations in Pundit are RDF triples that users build starting from web page elements, as text or images. Annotations can be shared and developers can access and combine them into RDF knowledge graphs, while authorship of each triple is always retrievable. In this demo we showcase Pundit 2.0 and demonstrate how it can be used to enhance a digital library, by providing a data crowdsourcing platform. Pundit enables users to annotate different kind of entities and to contribute to the collaborative creation of a knowledge graph. This, in turn, refines in real-time the exploration functionalities of the library's faceted search, providing an immediate added value out of the annotation effort. Ad-hoc configurations can be used to drive specific visualisations, like the persons timeline-map shown in this demo.
Curating a document collection via crowdsourcing with Pundit 2.0
Knowledge Engineering Group, TU Darmstadt
Reseach paper Semantic Data Management & Big Data 2: A survey of HTTP caching implementations on the open Semantic Web
Semantics and Data Analytics for Smart City Applications (SDA-SmartCity). We are witnessing an era where advancements in networking, software and hardware technologies are enabling billions of smart devices and objects to be connected to the Internet. While the new technologies pave the way towards a new generation of smart city applications and services, the variation in modality, quality, formats and representations of smart city data are amongst the crucial issues that hinder effective use of the real world data collected by the sensory devices. Typically in a smart city framework, higher­level abstractions need to be created from real world data streams using data analytic methods for event processing and knowledge extraction purposes in a scalable way. Additionally, Semantic Web technologies and Linked Data play a key role in providing interoperability, association analysis, information and knowledge extractions, and reasoning about trust, privacy, provenance, and security in smart city frameworks. This tutorial will explore different processing steps for smart city data from data acquisition and semantic annotation to event processing, information extraction and pattern analysis in different smart city scenarios and using available cutting­ edge tools.
Tutorial full day tutorial: SDA-SmartCity
Social: Welcome Reception - Terrace International
Reseach paper Machine Learning: Assigning Semantic Labels to Data Sources
(116) Ramnandan Krishnamurthy, Amol Mittal, Craig Knoblock and Pedro Szekely.
Reseach Track LinkedData & Data Management: LinkedData & Data Management
Learning cross-lingual semantic representations of relations from textual data is useful for tasks like cross-lingual information retrieval and question answering. So far, research has been mainly focused on cross-lingual entity linking, which is confined to linking between phrases in a text document and their corresponding entities in a knowledge base but cannot link to relations. In this paper, we present an approach for inducing clusters of semantically-related relations expressed in text, where relation clusters i) can be extracted from text of different languages, ii) are embedded in a semantic representation of the context, and iii) can be linked across languages to properties in a knowledge base. This is achieved by combining multi-lingual semantic role labeling (SRL) with cross-lingual entity linking followed by spectral clustering of the annotated SRL graphs. With our initial implementation we learned a cross-lingual library of relations from English and Spanish Wikipedia articles. To demonstrate its usefulness we apply it to cross-lingual question answering over linked data.
Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text
Developers Workshop. Like any technology, the Semantic Web crucially depends on its developers. Many of us have created SemWeb software, but only few have gotten the opportunity to talk about what is so special about the code they wrote. The ESWC2015 Developers Workshop therefore provides a forum for SemWeb and Linked Data developers. We unite developers on May 31st 2015 to discuss about topics we passionately care about: (i) How to develop applications on top of Linked Data? (ii) How can browser applications influence the Semantic Web? (iii) How to create libraries for technologies such as RDF (JSON-LD, Turtle …), SPARQL, PROV? (iv) What about mobile and native applications? (v) How to do semantic development for a specific domain? In other words, this workshop is about how you made things work. It is about implementations, methods, techniques, about how you solved practical problems for Linked Data.
LT3, Language and Translation Technology Team
(176) Jacobo Rouces, Gerard de Melo and Katja Hose.
Reseach paper Cognition & WebScience: FrameBase: Representing N-ary Relations using Semantic Frames