anchor.diff:  demo_22,22,John P. Mccrae,Mccrae,Yuzu: Publishing Any Data as Linked Data,demo,Poster and Demo session,47,John P. Mccrae,demo_22.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Linked data is one of the most important methods for improving the applicability of data, however most data is not in linked data formats and raising it to linked data is still a significant challenge. We present Yuzu, an application that makes it easy to host legacy data in JSON, XML or CSV as linked data, while providing a clean interface with advanced features. The ease-of-use of this framework is shown by its adoption for a number of existing datasets including WordNet.
   "
   demo_29,29,"Evgeny Kharlamov, Bernardo Cuenca Grau, Ernesto Jimenez-Ruiz, Steffen Lamparter, Gulnar Mehdi, Martin Ringsquandl, Yavor Nenov, Stephan Grimm, Mikhail Roshchin and Ian Horrocks",Kharlamov,SOMM: Industry Oriented Ontology Management Tool,demo,Poster and Demo session,17,Evgeny Kharlamov,demo_29.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"In this demo we present the SOMM system that resulted from an ongoing collaboration between Siemens and the University of Oxford. The goal of this collaboration is to facilitate design and management of ontologies that capture conceptual information models underpinning various industrial applications. SOMM supports engineers with little background on semantic technologies in the creation of such ontologies and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for ontology integration. We demonstrate functionality of SOMM on two scenarios from energy and manufacturing domains."
  -demo_95,95,"Atsuko Yamaguchi, Kouji Kozaki, Kai Lenz, Yasunori Yamamoto, Hiroshi Masuya and Norio Kobayashi",Yamaguchi,Data Acquisition by Traversing Class-Class Relationships over the Linked Open Data,demo,Poster and Demo session,22,Atsuko Yamaguchi,demo_95.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Linked Open Data (LOD) is a powerful mechanism for linking different datasets published on the Web, which is expected to create new value of data through mash-up over various datasets on the Web. One of the important needs to obtain data from LOD is to find a path of resources connecting given two classes, each of which has an end resource of the path. 
  +demo_95,95,"Atsuko Yamaguchi, Kouji Kozaki, Kai Lenz, Yasunori Yamamoto, Hiroshi Masuya and Norio Kobayashi",Yamaguchi,Data Acquisition by Traversing Class-Class Relationships over the Linked Open Data,poster,Poster and Demo session,22,Atsuko Yamaguchi,demo_95.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Linked Open Data (LOD) is a powerful mechanism for linking different datasets published on the Web, which is expected to create new value of data through mash-up over various datasets on the Web. One of the important needs to obtain data from LOD is to find a path of resources connecting given two classes, each of which has an end resource of the path. 
   In this study, the two technologies for the approach are introduced: a labeled multi graph named class graph to compute class-class relationships and an RDF specification named SPARQL Builder Metadata to obtain and store required metadata for construction of a class graph. In addition, as a practical application, we introduce the SPARQL Builder system, which assists users in writing semantic queries for LOD."
   doctoralconsortium_18,18,Radityo Eko Prasojo,Prasojo,Entity-Relationship Extraction from Wikipedia Unstructured Text ,doctoralconsortium,Session 1,1,Radityo Eko Prasojo,doctoralconsortium_18.pdf,,2016-10-18T11:00:00,2016-10-18T11:15:00,"Wikipedia has been the primary source of information for many automatically-generated Semantic Web data sources. However, they suffer from incompleteness since they largely do not cover information contained in the unstructured texts of Wikipedia. Our goal is to extract structured entity-relationships in RDF from such unstructured texts, ultimately using them to enrich existing data sources. Our extraction technique is aimed to be topic-independent, leveraging grammatical dependency of sentences and context semantic refinement. Preliminary evaluations of the proposed approach has shown some promising results."
   research_127,127,"Dilshod Ibragimov, Katja Hose, Torben Bach Pedersen and Esteban Zimanyi",Ibragimov,Optimizing Aggregate SPARQL Queries using Materialized RDF Views,research,Querying,3,Dilshod Ibragimov,research_127.pdf,502,2016-10-19T14:40:00,2016-10-19T15:00:00,"During the past couple of years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques, as proposed in relational databases, do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. Therefore, to overcome these challenges, this paper proposes MARVEL – the approach consisting of a view selection algorithm based on an RDF-specific cost model, a view definition syntax. and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that the approach can improve query response time by more than an order of magnitude and is able to efficiently handle RDF specifics."
  
positive.code: 
  To address the above challenges, this paper demonstrates SWoTSuite, a toolkit for prototyping SWoT applications. It hides the use of semantic web technologies as much as possible to avoid the burden of designing SWoT applications that involves designing ontologies, annotating sensor data, and using reasoning mechanisms to enrich data. Taking inspiration from sharing and reuse approaches, SWoTSuite reuses data and vocabularies. It leverages existing technologies to build applications. We take a hello world naturopathy application as an example and demonstrate an application development process using SWoTSuite. The demo video is available at URL- http://tinyurl.com/zs9flrt."
  demo_68,68,"Damien Graux, Louis Jachiet, Pierre Geneves and Nabil Layaida",Graux,SPARQLGX in Action: Efficient Distributed Evaluation of SPARQL with Apache Spark,demo,Poster and Demo session,35,Damien Graux,demo_68.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"We demonstrate SPARQLGX: our implementation of a distributed sparql evaluator. We show that SPARQLGX makes it possible to evaluate SPARQL queries on billions of triples distributed across multiple nodes, while providing attractive performance figures."
  demo_67,67,"Valeria Fionda, Melisachew Wudage Chekol and Giuseppe Pirrò",Fionda,Gize: A Time Warp in the Web of Data,demo,Poster and Demo session,43,Valeria Fionda,demo_67.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"We introduce the Gize framework for querying historical RDF data. Gize builds upon two main pillars: a lightweight approach to keep historical data, and an extension of SPARQL called SPARQ–LTL, which incorporates temporal logic primitives to enable a rich class of queries. One striking point of Gize is that its features can be readily made available in existing query processors."
  demo_66,66,"Hassan Saif, Miriam Fernandez, Matthew Rowe and Harith Alani",Saif,On the Role of Semantics for Detecting pro-ISIS Stances on Social Media,demo,Poster and Demo session,32,Hassan Saif,demo_66.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"From its start, the so-called Islamic State of Iraq and the Levant (ISIL/ISIS) has been successfully exploiting social media networks, most notoriously Twitter, to promote its propaganda and recruit new members, resulting in thousands of social media users adopting pro-ISIS stance every year. Automatic identification of pro-ISIS users on social media has, thus, become the centre of interest for various governmental and research organisations. In this paper we propose a semantic-based approach for radicalisation detection on Twitter. Unlike most previous works, which mainly rely on the lexical and contextual representation of the content published by Twitter users, our approach extracts and makes use of the underlying semantics of words exhibited by these users to identify their pro/anti-ISIS stances. Our results show that classifiers trained from words' semantics outperform those trained from lexical and network features by 2% on average F1-measure.
  "
  demo_65,65,"Jonas Bulegon Gassen, Stefano Faralli, Simone Paolo Ponzetto and Jan Mendling",Gassen,Who-Does-What: A knowledge base of people's occupations and job activities,demo,Poster and Demo session,20,Jonas Bulegon Gassen,demo_65.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"System analysis and design is concerned with the creation of conceptual models. In this paper, we introduce a novel resource called ""Who-Does-What"" (WDW) that supports the creation and quality assurance of such models. WDW provides a knowledge base of activities for classes of people engaged in a wide range of different occupations. The resource is semi-automatically created by populating the manually-created Standard Occupational Classification (SOC) of the US Department of Labor with activities found on the Web."
  demo_64,64,"Changlong Wang, Xiaowang Zhang and Zhiyong Feng",Wang,Structure-guiding Modular Reasoning for Expressive Ontologies,demo,Poster and Demo session,38,Changlong Wang,demo_64.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"We propose a technique that combine an OWL 2 EL reasoner with an OWL 2 reasoner to classify expressive ontologies. We exploit the information implied by the ontology structure to identify a small non-EL ontology that contains necessary axioms to ensure the completeness. In the process of ontology classification, the bulk of workload is delegated to an efficient OWL 2 EL reasoner and the small part of workload is handled by a less efficient OWL 2 reasoner. Experimental results show that our approach leads to a reasonable task assignment and offers a substantial speedup in ontology classification."
  demo_62,62,Michał Blinkiewicz and Jaroslaw Bak,Blinkiewicz,SQuaRE: A Visual Tool For Creating R2RML Mappings,demo,Poster and Demo session,09,Michał Blinkiewicz,demo_62.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"We present the recent progress of SQuaRE, the SPARQL
  Query and R2RML mappings Environment which provides a graphical
  interface for creating R2RML mappings which can be immediately tested
  by executing SPARQL queries. SQuaRE is a web-based tool with easy
  
negative.code: applications. We describe SQuaRE’s main features, its architecture as
  well as technical details."
  application_47,47,"Gregoire Burel, Lara S. G. Piccolo and Harith Alani",Burel,EnergyUse - A Collaborative Semantic Platform for Monitoring and Discussing Energy Consumption,application,Smart Planet,3,Gregoire Burel,application_47.pdf,504+505,2016-10-19T14:40:00,2016-10-19T15:00:00,"Conserving fossil-based energy to reduce carbon emissions is key to slowing down global warming. The 2015 Paris agreement on climate change emphasised the importance of raising public awareness and participation to address this societal challenge. In this paper we introduce EnergyUse; a social and collaborative platform for raising awareness on climate change, by enabling users to view and compare the actual energy consumption of various appliances, and to share and discuss energy conservation tips in an open and social environment. The platform collects data from smart plugs, and exports appliance consumption information and community generated energy tips as linked data. In this paper we report on the system design, data modelling, platform usage and early deployment with a set of 58 initial participants. We also discuss the challenges, lessons learnt, and future platform developments."
  demo_60,60,Thomas Wilmering and Mark B. Sandler,Wilmering,Interdisciplinary Classification of Audio Effects in the Audio Effect Ontology,poster,Poster and Demo session,47,Thomas Wilmering,demo_60.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"This paper discusses an extension to the Audio Effect Ontology (AUFX-O) for the interdisciplinary classification of audio effect types. The ontology extension implements a unified classification system that draws on knowledge from different music-related disciplines and is designed to facilitate the retrieval of audio effect information based on low-level and semantic aspects. It extends AUFX-O enabling communication between agents from different disciplines within the field of music creation and production. After briefly discussing the ontology, we show how it can be used to efficiently classify and retrieve effect types."
  resource_46,46,"Damien Graux, Louis Jachiet, Pierre Geneves and Nabil Layaida",Graux,SPARQLGX: Efficient Distributed Evaluation of SPARQL with Apache Spark,resource,Querying/SPARQL (I),2,Damien Graux,resource_46.pdf,501,2016-10-20T10:50:00,2016-10-20T11:10:00,"SPARQL is the W3C standard query language for querying data expressed in the Resource Description Framework (RDF). The increasing amounts of RDF data available raise a major need and research interest in building efficient and scalable distributed SPARQL query evaluators. In this context, we propose and share SPARQLGX: our implementation of a distributed RDF datastore based on Apache Spark. SPARQLGX is designed to leverage existing Hadoop infrastructures for evaluating SPARQL queries. SPARQLGX relies on a translation of SPARQL queries into executable Spark code that adopts evaluation strategies according to (1) the storage method used and (2) statistics on data. We show that SPARQLGX makes it possible to evaluate SPARQL queries on billions of triples distributed across multiple nodes, while providing attractive performance figures. We report on experiments which show how SPARQLGX compares to related state-of-the-art implementations. Using a simple design, SPARQLGX already represents an interesting alternative in several scenarios. We share it as a resource for the further construction of efficient SPARQL evaluators."
  resource_45,45,"Monika Solanki, Bojan Božić, Markus Freudenberg, Rob Brennan and Dimitris Kontokostas",Solanki,Enabling combined software and data engineering at Web-scale: The ALIGNED suite of ontologies,resource,Ontologies (II),1,Monika Solanki,resource_45.pdf,504+505,2016-10-21T13:30:00,2016-10-21T13:50:00,"Effective, collaborative integration of software services and big data to develop insightful analytics, for Web-scale systems, is now a crucial techno-economic challenge. This requires new combined data and software engineering processes and tools. Semantic metadata standards such as RDFS and OWL, and linked data principles, provide a technical grounding for such integrated systems given an appropriate model of the domain. In this paper we introduce the ALIGNED suite of ontologies or vocabularies specifically designed to model the information exchange needs of combined software and data engineering processes. The models have been deployed to enable: tool-chain integration, such as the exchange of data quality reports; cross-domain communication, such as interlinked data and software unit testing; mediation of the system design process through the capture of design intents and as a source of context for model-driven software engineering processes. These ontologies are deployed in trial live web-scale, data-intensive system development environments in both the commercial and academic domains. We exemplify the usage of the suite on a complex collaborative software and data engineering scenario from the legal information system domain."
  poster_110,110,"Ben De Meester, Anastasia Dimou, Ruben Verborgh, Erik Mannens and Rik Van de Walle",Meester,Discovering and Using Functions via Content Negotiation,poster,Poster and Demo session,41,Ben De Meester,poster_110.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Data has been made reusable and machine-interpretable by publishing it as Linked Data.
  