anchor.diff:  
   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
  +demo_67,67,"Valeria Fionda, Melisachew Wudage Chekol and Giuseppe Pirrò",Fionda,Gize: A Time Warp in the Web of Data,poster,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,poster,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,poster,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,poster,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,poster,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
  
positive.code: journal_9,9,Danh Le-Phuoc,Le-Phuoc,Operator-aware approach for boosting performance in RDF stream processing,journal,Streams,3,Danh Le-Phuoc,http://dx.doi.org/10.1016/j.websem.2016.04.001,501,2016-10-20T14:10:00,2016-10-20T14:30:00,"To enable efficiency in stream processing, the evaluation of a query is usually performed over bounded parts of (potentially) unbounded streams, i.e., processing windows “slide” over the streams. To avoid inefficient re-evaluations of already evaluated parts of a stream in respect to a query, incremental evaluation strategies are applied, i.e., the query results are obtained incrementally from the result set of the preceding processing state without having to re-evaluate all input buffers. This method is highly efficient but it comes at the cost of having to maintain processing state, which is not trivial, and may defeat performance advantages of the incremental evaluation strategy. In the context of RDF streams the problem is further aggravated by the hard-to-predict evolution of the structure of RDF graphs over time and the application of sub-optimal implementation approaches, e.g., using relational technologies for storing data and processing states which incur significant performance drawbacks for graph-based query patterns. To address these performance problems, this paper proposes a set of novel operator-aware data structures coupled with incremental evaluation algorithms which outperform the counterparts of relational stream processing systems. This claim is demonstrated through extensive experimental results on both simulated and real datasets."
  journal_8,8,"Marco Rospocher, Marieke van Erp, Piek Vossen, Antske Fokkens, Itziar Aldabe, German Rigau, Aitor Soroa, Thomas Ploeger, Tessel Bogaard",Rospocher,Building event-centric knowledge graphs from news,journal,Natural Language Processing,4,Marco Rospocher,http://dx.doi.org/10.1016/j.websem.2015.12.004,504+505,2016-10-21T11:30:00,2016-10-21T11:50:00,"Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations–e.g. a celebrity’s birth date, occupation and family members–obtained from large repositories such as Freebase or Wikipedia. In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the world through the events they report, we present an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques. Such ECKGs capture long-term developments and histories on hundreds of thousands of entities and are complementary to the static encyclopedic information in traditional knowledge graphs. We describe our event-centric representation schema, the challenges in extracting event information from news, our open source pipeline, and the knowledge graphs we have extracted from four different news corpora: general news (Wikinews), the FIFA world cup, the Global Automotive Industry, and Airbus A380 airplanes. Furthermore, we present an assessment on the accuracy of the pipeline in extracting the triples of the knowledge graphs. Moreover, through an event-centered browser and visualization tool we show how approaching information from news in an event-centric manner can increase the user’s understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts."""
  demo_30,30,"Rivindu Perera, Parma Nand and Gisela Klette",Perera,Lexicalizing DBpedia with Realization Enabled Ensemble Architecture: RealText-lex2 Approach,demo,Poster and Demo session,26,Rivindu Perera,demo_30.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"DBpedia encodes massive amounts of open domain knowledge and is growing by accumulating more triples at the same rate as Wikipedia. However, in order to be able to present the knowledge processed using DBpedia, the applications need to present this knowledge often require natural language formulations of these triples. The RealText-lex2 framework offers a scalable platform to transform these triples to natural language sentences using lexicalization patterns. The framework has evolved from its previous version (RealText-lex) and is comprised of four lexicalization pattern mining modules which derive patterns from a training triple collection. These patterns can be then applied on the new triples given that they satisfy a defined set of constraints."
  demo_31,31,"Joo Sungmin, Seiji Koide, Hideaki Takeda, Daisuke Horyu, Akane Takezaki and Tomokazu Yoshida",Sungmin,Agriculture Activity Ontology : An ontology for core vocabulary of agriculture activity,demo,Poster and Demo session,21,Joo Sungmin,demo_31.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"This paper proposes Agriculture Activity Ontology(AAO) as a basis of the core vocabulary of agricultural activity. Since concepts of agriculture activities are formed by the various context such as purpose, means, crop, and field, we organize the agriculture activity ontology as a hierarchy of concepts discriminated by various properties such as purpose, means, crop and field. The vocabulary of agricultural activity is then defined as the subset of the ontology. Since the ontology is consistent, extendable, and capable of some inferences thanks to Description Logics, so the vocabulary inherits these features. The vocabulary is also linked to existing vocabularies such as AGROVOC. It is expected to use in the data format in the agricultural IT system. The vocabulary is adopted as the part of ""the guideline for agriculture activity names for agriculture IT systems"" issued by Ministry of Agriculture, Forestry and Fisheries (MAFF), Japan."
  demo_32,32,"Rivindu Perera, Parma Nand and Gisela Klette",Perera,Enriching Answers in Question Answering Systems using Linked Data,demo,Poster and Demo session,23,Rivindu Perera,demo_32.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Linked Data has emerged as the most widely used and the most powerful knowledge source for Question Answering (QA). Although Question Answering using Linked Data (QALD) fills in many gaps in the traditional QA models, the answers are still presented as factoids. This research introduces an answer presentation model for QALD by employing Natural Language Generation (NLG) to generate natural language descriptions to present an informative answer. The proposed approach employs lexicalization, aggregation, and referring expression generation to build a human-like enriched answer utilizing the triples extracted from the entities mentioned in the question as well as the entities contained in the answer."
  demo_33,33,"Stasinos Konstantopoulos, Angelos Charalambidis, Giannis Mouchakis, Antonis Troumpoukis, Jürgen Jakobitsch and Vangelis Karkaletsis",Konstantopoulos,Semantic Web Technologies and Big Data Infrastructures: SPARQL Federated Querying of Heterogeneous Big Data Stores,demo,Poster and Demo session,27,Stasinos Konstantopoulos,demo_33.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"The ability to cross-link large scale data with each other and with structured Semantic Web data, and the ability to uniformly process Semantic Web and other data adds value to both the Semantic Web and to the Big Data community. This paper presents work in progress towards integrating Big Data infrastructures with Semantic Web technologies, allowing for the cross-linking and uniform retrieval of data stored in both Big Data infrastructures and Semantic Web data. The technical challenges involved in achieving this, pertain to both data and system interoperability: we need a way to make the semantics of Big Data explicit so that they can interlink and we need a way to make it transparent for the client applications to query federations of such heterogeneous systems. The paper presents an extension of the Semagrow federated SPARQL query processor that is able to seamlessly federated SPARQL endpoints, Cassandra databases, and Solr databases, and discusses future directions of this line of work."
  demo_34,34,"Kai Lenz, Hiroshi Masuya and Norio Kobayashi",Lenz,RIKEN MetaDatabase: a database publication platform for RIKENs life-science researchers that promotes research collaborations over different research area,demo,Poster and Demo session,24,Kai Lenz,demo_34.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"To promote data dissemination and integration of life science
  datasets produced in a general research institute, RIKEN, we developed
  an infrastructure database named as ""RIKEN MetaDatabase"", which en-
  ables data publication and integration with Resource Description Frame-
  
negative.code: began to accelerated. Combined with global standardisation activities,
  we expect this database can contribute data integration across the world."
  demo_35,35,Adam Sotona,Sotona,How to feed Apache HBase with petabytes of RDF data: An extremely scalable RDF store based on Eclipse RDF4J framework and Apache HBase database,demo,Poster and Demo session,31,Adam Sotona,demo_35.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Eclipse RDF4J (formerly known as Sesame) is an open source Java framework for processing RDF data. RDF4J framework is extensible through its Storage And Inference Layer (SAIL) to support various RDF stores and inference engines. Apache HBase is the Hadoop database, a distributed and scalable big data store. It is designed to scale up from single servers to thousands of machines. We have connected RDF4J and HBase to receive an extremely scalable RDF store."
  demo_36,36,"Wei Emma Zhang, Ermyas Abebe, Quan Z. Sheng and Kerry Taylor",Zhang,Towards Building Open Knowledge Base From Programming Question-Answering Communities,poster,Poster and Demo session,13,Wei Emma Zhang,demo_36.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"In this paper, we propose the rst system, so-called Open Programming Knowledge Extraction (OPKE), to automatically extract knowledge from programming Question-Answering (QA) communities. OPKE is the rst step of building a programming-centric knowledge base. Data mining and Natural Language Processing techniques are leveraged to identify paraphrased questions and construct structured information. Preliminary evaluation shows the eectiveness of OPKE."
  demo_37,37,Gerhard Wohlgenannt and Filip Minic,Wohlgenannt,Using word2vec to Build a Simple Ontology Learning System,poster,Poster and Demo session,15,Gerhard Wohlgenannt,demo_37.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Ontology learning has been an important research area in the Semantic Web field in the last 20 years. Ontology learning systems generate domain models from data (typically text) using a combination of sophisticated methods. In this poster, we study the use of Google's word2vec to emulate a simple ontology learning system, and compare the results to an existing ""traditional"" ontology learning system.
  "
  resource_1,1,"Petar Ristoski, Gerben Klaas Dirk de Vries and Heiko Paulheim",Ristoski,A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web,resource,Embeddings & Neural Approaches,2,Petar Ristoski,resource_1.pdf,502,2016-10-20T15:50:00,2016-10-20T16:10:00,"In the recent years, several approaches for machine learning on the Semantic Web have been proposed. However, no extensive comparisons between those approaches have been undertaken, in particular due to a lack of publicly available, acknowledged benchmark datasets. In this paper, we present a collection of 22 benchmark datasets at different sizes, derived from existing Semantic Web datasets as well as from external classification and regression problems linked to datasets in the Linked Open Data cloud. Such a collection of datasets can be used to conduct qualitative performance testing and systematic comparisons of approaches."
  demo_93,93,"Satoshi Kume, Hiroshi Masuya, Yosky Kataoka and Norio Kobayashi",Kume,Development of an Ontology for an Integrated Image Analysis Platform to enable Global Sharing of Microscopy Imaging Data,poster,Poster and Demo session,23,Satoshi Kume,demo_93.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Imaging data are fundamental to life sciences. We aimed to construct a microscopy ontology for an integrated metadata database of optical and electron microscopy images combined with various bio-entities. To realise this, we applied the Resource Description Framework (RDF) to an Open Microscopy Environment (OME) data model, which is the de facto standard to describe optical microscopy images and experimental data. We translated the XML-based OME metadata into the base concept of Web Ontology Language (OWL) as a trial of developing microscopy ontology. We describe the OWL-based ontology of microscopy imaging data and propose 18 upper-level concepts of ontology with missing concepts such as electron microscopy, phenotype data, biosample, and imaging conditions."
  demo_90,90,"Yuting Song, Taisuke Kimura, Biligsaikhan Batjargal and Akira Maeda",Song,Cross-Language Record Linkage using Word Embedding driven Metadata Similarity Measurement,poster,Poster and Demo session,14,Yuting Song,demo_90.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"Aiming to link the records that refer to the same entity across multiple databases in different languages, we address the mismatches of wordings between literal translations of metadata in source language and metadata in target language, which cannot be calculated by string-based measures. In this paper, we propose a method based on word embedding, which can capture the semantic similarity relationships among words. The effectiveness of this method is confirmed in linking the same records between Ukiyo-e (Japanese traditional woodblock printing) databases in Japanese and English. This method could be applied to other languages since it makes little assumption about languages."
  demo_91,91,Robin Keskisärkkä,Keskisärkkä,Representing RDF Stream Processing Queries in RSP-SPIN,demo,Poster and Demo session,43,Robin Keskisärkkä,demo_91.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"A number of RDF Stream Processing (RSP) systems have been developed to support the processing of streaming Linked Data, however, due to the lack of a standardized query language they all provide different SPARQL extensions. The RSP Community Group is in the process of developing a standardized RSP query language (RSP-QL), which incorporates many of features of existing RSP language extensions. In this demo paper we describe how RSP-SPIN, a SPIN extension for representing RSP-QL queries, can be used to encapsulate RSP queries as RDF, forming a syntax agnostic representation that can be used to support serialization into multiple RSP language extensions. This could be useful, for example, to reduce the effort required to produce and maintain RSP benchmarks, since developers can focus on a single representation per query, rather than manually implementing and validating queries for several languages in parallel. "
  demo_96,96,"Amna Basharat, Khaled Rasheed and I. Budak Arpinar",Basharat,Harnessing Crowds and Experts for Semantic Annotation of the Qur'an,poster,Poster and Demo session,31,Amna Basharat,demo_96.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"In this paper we illustrate how we harness the power of crowds and specialized experts through automated knowledge acquisition workflows for semantic annotation in specialized and knowledge intensive domains. We undertake the special case of the Arabic script of the Qur'an, a widely studied manuscript, and apply a hybrid methodology of traditional 'crowdsourcing' augmented with 'expertsourcing' for semantically annotating its verses. We demonstrate that our proposed hybrid method presents a promising approach for achieving reliable annotations in an efficient and scalable manner, especially in cases where a high level of accuracy is required in knowledge intense and sensitive domains. "
  resource_4,4,"Thomas Rebele, Fabian M. Suchanek, Johannes Hoffart, Joanna Biega, Erdal Kuzey and Gerhard Weikum",Rebele,"YAGO: a multilingual knowledge base from Wikipedia, Wordnet, and Geonames",resource,Multilinguality,1,Thomas Rebele,resource_4.pdf,501,2016-10-19T14:00:00,2016-10-19T14:20:00,"YAGO is a large knowledge base that is built automatically from Wikipedia, WordNet and GeoNames. The project combines information from 10 Wikipedias of different languages, thus giving the knowledge a multilingual dimension. It also attaches spatial and temporal information to many facts, and thus allows the user to query the data over space and time. YAGO focuses on extraction quality and achieves a manually evaluated precision of 95%. In this paper, we explain from a general perspective how YAGO is built from its sources, how its quality is evaluated, how a user can access it, and how other projects utilize it."
  demo_94,94,Motoyuki Takaai and Yohei Yamane,Takaai,Constructing Semantic Networks of Development Activities from Weekly Reports,poster,Poster and Demo session,16,Motoyuki Takaai,demo_94.pdf,3rd Floor,2016-10-19T18:00:00,2016-10-19T21:00:00,"In the development departments of some manufacturing companies,
  there are weekly reports describing the status of events but they are poorly structured plain texts.
  In this report, we propose a method for constructing semantic networks of development activities from weekly reports.
  Our ontology-based method extracts things like events, status and agents from the reports and constructs relations
  