anchor.diff:  The peculiarity of this system is to exploit semantic information of the concept's context for improving the quality of label translations.
   The system has been tested both within the Organic.Lingua project by translating the modeled ontology in three languages and on other multilingual ontologies in order to evaluate the effectiveness of the system in other contexts.
   The results have been compared with the translations provided by the Microsoft Translator API and the improvements demonstrated the viability of the proposed approach."
  -application_burel_47,47,"Grégoire Burel, Lara S. G. Piccolo and Harith Alani",burel,EnergyUse - A Collaborative Semantic Platform for Monitoring and Discussing Energy Consumption,application,Smart Planet,3,Grégoire Burel,paper_A47_.pdf,503,2016-10-19T14:00:00,2016-10-19T15:20:00,"SPARQL has many nice features for accessing data integrated across different data sources, which is an important step in any data analysis task.  We report the use of SPARQL for two real data analytic use cases from the healthcare and life sciences domains, which exposed certain weaknesses in the current specification of SPARQL; specifically when the data being integrated is most conveniently accessed via RESTful services and in formats beyond RDF, such as XML. We therefore extended SPARQL with generalized 'service', constructs for accessing services beyond the SPARQL endpoints supported by 'service'; for efficiency, our constructs additionally needed to support posting data, which is also not supported by 'service'.   Furthermore, data from multiple sources led to natural modularity in the queries, with different portions of the query pertaining to different sources, so we also extended SPARQL with a simple 'function' mechanism to isolate the mechanics of accessing each endpoint.  We provide an open source implementation of this SPARQL endpoint in an RDF store called Quetzal, and evaluate its use in the two data analytic scenarios over real datasets."
  +application_burel_47,47,"Grégoire Burel, Lara S. G. Piccolo and Harith Alani",burel,EnergyUse - A Collaborative Semantic Platform for Monitoring and Discussing Energy Consumption,application,Smart Planet,3,Grégoire Burel,paper_A47_.pdf,504+505,2016-10-19T14:00:00,2016-10-19T15:20:00,"SPARQL has many nice features for accessing data integrated across different data sources, which is an important step in any data analysis task.  We report the use of SPARQL for two real data analytic use cases from the healthcare and life sciences domains, which exposed certain weaknesses in the current specification of SPARQL; specifically when the data being integrated is most conveniently accessed via RESTful services and in formats beyond RDF, such as XML. We therefore extended SPARQL with generalized 'service', constructs for accessing services beyond the SPARQL endpoints supported by 'service'; for efficiency, our constructs additionally needed to support posting data, which is also not supported by 'service'.   Furthermore, data from multiple sources led to natural modularity in the queries, with different portions of the query pertaining to different sources, so we also extended SPARQL with a simple 'function' mechanism to isolate the mechanics of accessing each endpoint.  We provide an open source implementation of this SPARQL endpoint in an RDF store called Quetzal, and evaluate its use in the two data analytic scenarios over real datasets."
   application_charron_21,21,"Bruno Charron, Yu Hirate, David Purcell and Martin Rezk",charron,Extracting Semantic Information for e-Commerce,application,Enriching Data Sources,4,Bruno Charron,paper_A21_.pdf,502,2016-10-21T15:30:00,2016-10-21T16:50:00,"In model-based systems engineering a model specifying the system's design is shared across a variety of disciplines and used to ensure the consistency and quality of the overall design. Existing implementations for describing these system models exhibit a number of shortcomings regarding their approach to data management. In this emerging applications paper, we present the application of an ontology for space system design providing increased semantic soundness of the underlying standardized data specification, enabling reasoners to identify problems in the system, and allowing the application of operational knowledge collected over past projects to the system to be designed. Based on a qualitative evaluation driven by data derived from an actual satellite design project, a reflection on the applicability of ontologies in the overall model-based systems engineering approach is pursued."
  -application_egami_54,54,"Shusaku Egami, Takahiro Kawamura and Akihiko Ohsuga",egami,Building Urban LOD for Solving Illegally Parked Bicycles in Tokyo,application,Smart Planet,2,Shusaku Egami,paper_A54_.pdf,503,2016-10-19T14:00:00,2016-10-19T15:20:00,"The illegal parking of bicycles is a social problem in Tokyo and other urban areas. The purpose of this study was to sustainably build Linked Open Data (LOD) for the illegally parked bicycles and to support the problem solving by raising social awareness, in cooperation with the Bureau of General Affairs of Tokyo. We first extracted information on the problem factors and designed LOD schema for illegally parked bicycles. Then we collected pieces of data from Social Networking Service (SNS) and websites of municipalities to build the illegally parked bicycle LOD (IPBLOD) with more than 200,000 triples. We then estimated the missing data in the LOD based on the causal relations from the problem factors. As a result, the number of illegally parked bicycles can be inferred with 70.9% accuracy. Finally, we published the complemented LOD and a Web application to visualize the distribution of illegally parked bicycles in the city. We hope this raises social attention on this issue."
  +application_egami_54,54,"Shusaku Egami, Takahiro Kawamura and Akihiko Ohsuga",egami,Building Urban LOD for Solving Illegally Parked Bicycles in Tokyo,application,Smart Planet,2,Shusaku Egami,paper_A54_.pdf,504+505,2016-10-19T14:00:00,2016-10-19T15:20:00,"The illegal parking of bicycles is a social problem in Tokyo and other urban areas. The purpose of this study was to sustainably build Linked Open Data (LOD) for the illegally parked bicycles and to support the problem solving by raising social awareness, in cooperation with the Bureau of General Affairs of Tokyo. We first extracted information on the problem factors and designed LOD schema for illegally parked bicycles. Then we collected pieces of data from Social Networking Service (SNS) and websites of municipalities to build the illegally parked bicycle LOD (IPBLOD) with more than 200,000 triples. We then estimated the missing data in the LOD based on the causal relations from the problem factors. As a result, the number of illegally parked bicycles can be inferred with 70.9% accuracy. Finally, we published the complemented LOD and a Web application to visualize the distribution of illegally parked bicycles in the city. We hope this raises social attention on this issue."
   application_hennig_6,6,"Christian Hennig, Alexander Viehl, Benedikt Kämpgen and Harald Eisenmann",hennig,Ontology-Based Design of Space Systems,application,Ontologies (I),4,Christian Hennig,paper_A6_.pdf,501,2016-10-20T15:30:00,2016-10-20T16:50:00,"We present a domain-agnostic system for Question Answering over multiple semi-structured and possibly linked datasets without the need of a training corpus. The system is motivated by an industry use-case where Enterprise Data needs to be combined with a large body of Open Data to fulfill information needs not satisfied by prescribed application data models. Our proposed Question Answering pipeline combines existing components with novel methods to perform, in turn, linguistic analysis of a query, named entity extraction, entity / graph search, fusion and ranking of possible answers. We evaluate QuerioDALI with two open-domain benchmarks and a biomedical one over Linked Open Data sources, and show that our system produces comparable results to systems that require training data and are domain-dependent. In addition, we analyze the current challenges and shortcomings."
   application_kharlamov_13,13,"Evgeny Kharlamov, Bernardo Cuenca Grau, Ernesto Jiménez-Ruiz, Steffen Lamparter, Gulnar Mehdi, Martin Ringsquandl, Yavor Nenov, Stephan Grimm, Mikhail Roshchin and Ian Horrocks",kharlamov,Capturing Industrial Information Models with Ontologies and Constraints,application,Ontologies (I),2,Evgeny Kharlamov,paper_A13_.pdf,501,2016-10-20T15:30:00,2016-10-20T16:50:00,"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."
   application_kharlamov_18,18,"Evgeny Kharlamov, Yannis Kotidis, Theofilos Mailis, Christian Neuenstadt, Charalampos Nikolaou, Özgür Lütfü Özcep, Christoforos Svingos, Dmitriy Zheleznyakov, Sebastian Brandt, Ian Horrocks, Steffen Lamparter, Yannis Ioannidis and Ralf Möller",kharlamov,Towards Analytics Aware Ontology Based Access to Static and Streaming Data,application,Ontologies (I),3,Evgeny Kharlamov,paper_A18_.pdf,501,2016-10-20T15:30:00,2016-10-20T16:50:00,"Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data."
  
positive.code: each of them with their own expressivity.
  However, there is no generic client available that can handle querying over multiple interfaces.
  This increases the complexity of combining datasets and designing new interfaces.
  One can imagine the difficulties that arise
  when trying to create a client querying various interfaces at the same time,
  that can be discovered just in time.
  To this end, I aim to design a generic Linked Data querying engine
  capable of handling different interfaces that can easily be extended.
  
negative.code: research_siow_9,9,"Eugene Siow, Thanassis Tiropanis and Wendy Hall",siow,SPARQL-to-SQL on Internet of Things Databases and Streams,research,Streams,2,Eugene Siow,paper_M29_.pdf,501,2016-10-20T13:30:00,2016-10-20T14:50:00,"To realise a semantic Web of Things, the challenge of achieving efficient Resource Description Format (RDF) storage and SPARQL query performance on Internet of Things (IoT) devices with limited resources has to be addressed. State-of-the-art SPARQL-to-SQL engines have been shown to outperform RDF stores on some benchmarks. In this paper, we describe an optimisation to the SPARQL-to-SQL approach, based on a study of time-series IoT data structures, that employs metadata abstraction and efficient translation by reusing existing SPARQL engines to produce Linked Data `just-in-time'. We evaluate our approach against RDF stores, state-of-the-art SPARQL-to-SQL engines and streaming SPARQL engines, in the context of IoT data and scenarios. We show that storage efficiency, with succinct row storage, and query performance can be improved from 2 times to 3 orders of magnitude."
  research_suchanek_10,10,"Fabian M. Suchanek, Colette Menard, Meghyn Bienvenu and Cyril Chapellier",suchanek,Can you imagine... a language for combinatorial creativity?,research,Knowledge Representation,3,Fabian M. Suchanek,paper_M49_.pdf,502,2016-10-19T11:00:00,2016-10-19T12:20:00," Combinatorial creativity combines existing concepts in a novel way in order to produce a new concept. For example, we can imag- ine jewelry that measures blood pressure. For this, we would combine the concept of jewelry with the capabilities of medical devices. Combinato- rial creativity can be used to develop new business ideas, to find plots for books or movies, or simply to disrupt conventional thinking. In this paper, we propose a formal language for combinatorial creativity, based on description logics. We show that our language can be used to model existing inventions and (to a limited degree) to generate new concepts.
  "
  research_taheriyan_30,30,"Mohsen Taheriyan, Craig Knoblock, Pedro Szekely and José Luis Ambite",taheriyan,Leveraging Linked Data to Discover Semantic Relations within Data Sources,research,Enriching Data Sources,2,Mohsen Taheriyan,paper_M107_.pdf,502,2016-10-21T15:30:00,2016-10-21T16:50:00,"Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, Yago and Wikidata. These KGs are inevitably bound to be incomplete. To fill in the gaps, data correlations in the KG can be analyzed to infer Horn rules and to predict new facts. However, Horn rules do not take into account possible exceptions, so that predicting facts via such rules introduces errors. 
  To overcome this problem,  we present a method for effective revision of learned Horn rules by effectively incorporating exceptions (i.e., negated atoms) into their bodies. This way errors are largely reduced. We apply our method to discover rules with exceptions from real-world KGs. Our experimental results demonstrate the effectiveness of the developed method and the improvements in accuracy for KG completion by rule-based fact prediction."
  research_third_1,1,"Allan Third, George Gkotsis, Eleni Kaldoudi, George Drosatos, Nick Portokallidis, Stefanos Roumeliotis, Kalliopi Pafilis and John Domingue",third,Integrating medical scientific knowledge with the semantically Quantified Self,research,Medical Applications,1,Allan Third,paper_M305_.pdf,501,2016-10-21T13:30:00,2016-10-21T14:50:00,"In recent years RDF and OWL have become the most common knowledge representation languages in use on the Web, propelled by the recommendation of the W3C. In this paper we examine an alternative way to represent knowledge based on Prototypes. This Prototype based representation has different properties, which we argue to be 
  more suitable for data sharing and reuse on the Web. Prototypes avoid the distinction between classes and instances and provide means for objects based data sharing and reuse.
  
  In this paper we discuss the requirements and design principles for Knowledge Representation based on Prototypes on the Web, after which we propose a formal syntax and semantics. We show how to embed knowledge representation based on Prototypes in the current Semantic Web standard stack. An implementation and practical evaluation of the system is presented in a separate resource paper."
  research_tiddi_14,14,"Ilaria Tiddi, Mathieu d'Aquin and Enrico Motta",tiddi,Learning to Assess Linked Data Relationships Using Genetic Programming,research,Linked Data Measurement,2,Ilaria Tiddi,paper_M167_.pdf,504+505,2016-10-20T13:30:00,2016-10-20T14:50:00,"Mapping data to a shared domain ontology is a key step in publishing semantic content on the Web. Most of the work on automatically mapping structured and semi-structured sources to ontologies focuses on semantic labeling, i.e., annotating data fields with ontology classes and/or properties. However, a precise mapping that fully recovers the intended meaning of the data needs to describe the semantic relations between the data fields too. We present a novel approach to automatically discover the semantic relations within a given data source. We mine the small graph patterns occurring in Linked Open Data and combine them to build a graph that will be used to infer semantic relations. We evaluated our approach on datasets from different domains. Mining patterns of maximum length five, our method achieves an average precision of 75% and recall of 77% for a dataset with very complex mappings to the domain ontology, increasing up to 86% and 82%, respectively, for simpler ontologies and mappings."
  research_wudage_25,25,Melisachew Wudage Chekol and Giuseppe Pirrò,wudage,Containment of Expressive SPARQL Navigational Queries,research,Querying,2,Melisachew Wudage Chekol ,research_wudage_25.pdf,502,2016-10-19T14:00:00,2016-10-19T15:20:00,"Query containment is one of the building block of query optimization techniques. In the relational world, query containment is a well-studied problem. At the same time it is well-understood that relational queries are not enough to cope with graph-structured data, where one is interested in expressing queries that capture navigation in the graph. This paper contributes a study on the problem of query containment for an expressive class of navigational queries called Extended Property Paths (EPPs). EPPs are more expressive than previous navigational extensions of SPARQL like property paths and nested regular expressions, for which containment has already been studied. We attack the problem of EPPs (and SPARQL with EPPs) containment and provide complexity bounds.
  "
  research_zhang_20,20,"Lei Zhang, Achim Rettinger and Ji Zhang",zhang,A Probabilistic Model for Time-Aware Entity Recommendation,research,Search (I),4,Lei Zhang,paper_M88_.pdf,502,2016-10-20T13:30:00,2016-10-20T14:50:00,"In recent years, there has been an increasing efforts to develop techniques for related entity recommendation, where the task is to retrieve a ranked list of related entities given a keyword query. Another trend in the area of information retrieval (IR) is to take temporal aspects of a given query into account when assessing the relevance of documents. However, while this has become an established functionality in document search engines, the significance of time, especially when explicitly given, has not been recognized for entity recommendation, yet. We address this gap by introducing the task of time-aware entity recommendation. We propose the first probabilistic model that takes time-awareness into consideration for entity recommendation by leveraging heterogeneous knowledge of entities extracted from different data sources publicly available on the Web. We extensively evaluate the proposed approach and our experimental results show considerable improvements compare to time-agnostic entity recommendation approaches."
  research_zhang_21,21,"Lei Zhang, Achim Rettinger and Ji Zhang",zhang,A Knowledge Base Approach to Cross-lingual Keyword Query Interpretation,research,Search (I),3,Lei Zhang,paper_M90_.pdf,502,2016-10-20T13:30:00,2016-10-20T14:50:00,"The amount of entities in large knowledge bases available on the Web has been increasing rapidly, making it possible to propose new ways of intelligent information access. In addition, there is an impending need for technologies that can enable cross-lingual information access. As a simple and intuitive way of specifying information needs, keyword queries enjoy widespread usage, but suffer from the challenges including ambiguity, incompleteness and cross-linguality. In this paper, we present a knowledge base approach to cross-lingual keyword query interpretation by transforming keyword queries in different languages to their semantic representation, which can facilitate query disambiguation and expansion, and also bridge the language barriers of queries. The experimental results show that our approach achieves both high efficiency and effectiveness and considerably outperforms the baselines."
  research_zhang_38,38,"Xiaowang Zhang, Zhiyong Feng, Xin Wang, Guozheng Rao and Wenrui Wu",zhang,Context-Free Path Queries on RDF Graphs,research,Querying,4,Xiaowang Zhang,paper_M120_.pdf,502,2016-10-19T14:00:00,2016-10-19T15:20:00,"Navigational graph queries are an important class of queries that can extract implicit binary relations over the nodes of input graphs. Most of the navigational query languages used in the RDF community, e.g. property paths in W3C SPARQL 1.1 and nested regular expressions in nSPARQL, are based on the regular expressions. It is known that regular expressions have limited expressivity; for instance, some natural queries, like same generations-queries} are not expressible with regular expressions. To overcome this limitation, in this paper, we present cfSPARQL, an extension of SPARQL query language equipped with context-free grammars. The cfSPARQL language is strictly more expressive than property paths and nested expressions. The additional expressivity can be used for modelling graph similarities, graph summarization and ontology alignment. Despite the increasing expressivity, we show that cfSPARQL still enjoys a low computational complexity and can be evaluated efficiently."
  research_zhu_22,22,"Linhong Zhu, Majid Ghasemi-Gol, Pedro Szekely, Aram Galstyan and Craig Knoblock",zhu,Unsupervised Entity Resolution on Multi-type Graphs,research,Ontology Matching,4,Linhong Zhu,paper_M270_.pdf,504+505,2016-10-20T10:30:00,2016-10-20T11:50:00,"We address the problem of performing entity resolution on RDF graphs containing multiple types of nodes, using the links between instances of different types to improve the accuracy. For example, in a graph of products and manufacturers the goal is to resolve all the products and all the manufacturers. We formulate this problem as multi-type graph summarization problem, which involves clustering the nodes in each type that refer to the same entity into one super node and creating weighted links among super nodes that summarize the inter-cluster links in the original graph. Experiments show that the proposed approach outperforms several state-of-the-art generic entity resolution approaches, especially in data sets with one-to-many, many-to-many relations and attributes with missing values."
  resource_allik_16,16,"Alo Allik, György Fazekas and Mark Sandler",allik,Ontological Representation of Audio Features,resource,Ontologies (II),3,Alo Allik,paper_R59_.pdf,504+505,2016-10-21T13:30:00,2016-10-21T14:50:00,"Feature extraction algorithms in Music Informatics aim at deriving statistical and semantic information directly from audio signals. These may be ranging from energies in several frequency bands to musical information such as key, chords or rhythm. There is an increasing diversity and complexity of features and algorithms in this domain and applications call for a common structured representation to facilitate interoperability, reproducibility and machine interpretability. We propose a solution relying on Semantic Web technologies that is designed to serve a dual purpose (1) to represent computational workflows of audio features and (2) to provide a common structure for feature data to enable the use of Open Linked Data principles and technologies in Music Informatics. The Audio Feature Ontology is based on the analysis of existing tools and music informatics literature, which was instrumental in guiding the ontology engineering process. The ontology provides a descriptive framework for expressing different conceptualisations of the audio feature extraction domain and enables designing linked data formats for representing feature data. In this paper, we discuss important modelling decisions and introduce a harmonised ontology library consisting of modular interlinked ontologies that describe the different entities and activities involved in music creation, production and publishing."
  resource_burns_15,15,"Gully Burns, Ulf Hermjakob and José Luis Ambite",burns,Abstract Meaning Representations as Linked Data,resource,Natural Language Processing,2,Gully Burns,paper_R58_.pdf,504+505,2016-10-21T10:30:00,2016-10-21T11:50:00,"Significant advances in Natural Language Processing (NLP) research are fostered when high-quality annotated corpora were provided for general use. In an effort to develop a sembank (i.e., an annotated corpus dedicated to capturing the semantic meaning of a large set of annotated sentences), NLP researchers have developed the Abstract Meaning Representation (AMR) formulation. Each AMR is a rooted, labeled graph that represents the semantics of a single sentence. Nodes in the core AMR graph represent concepts/entities (such as nouns, PropBank frames, etc.) and edges represent relations between concepts, (such a frame-specific arguments, roles, etc.). AMRs have been used to annotate corpora of classic books, newstext and the biomedical research literature. Research is progressing on creating automatic parsers to generate AMRs directly from textual input. In the work described here, we map the AMR representation to a linked data format (AMR-LD), adopting the ontological formulation of the underlying AMR faithfully. We describe the process of generating AMR-LD data from standard AMRs derived from biomedical research articles, including mapping named entities to well-known linked-data resources, such as Uniprot and PubChem, as well as an open-source software to convert AMR data to RDF. We describe the benefits of AMR-LD, including convenient analysis using SPARQL queries and ontology inferences, and embedding into the web of Linked Data. Finally, we discuss the possible impact of semantic web representations that are directly derived from natural language."
  resource_daniele_3,3,"Laura M. Daniele, Monika Solanki, Frank Den Hartog and Jasper Roes",daniele,Interoperability for Smart Appliances in the IoT World,resource,Smart Planet,4,Laura M. Daniele,paper_R13_.pdf,504+505,2016-10-19T14:00:00,2016-10-19T15:20:00,"Household appliances are set to become highly intelligent, smart and networked devices in the near future. Systematically deployed on the Internet of Things (IoT), they would be able to form complete energy consuming, producing, and managing ecosystems. Smart systems are technically very heterogeneous, and standardized interfaces on a sensor and device level are therefore needed. However, standardization in IoT has largely focused at the technical communication level, leading to a large number of different solutions based on various standards and protocols, with limited attention to the common semantics contained in the message data structures exchanged at the technical level. The Smart Appliance REFerence ontology (SAREF) is a shared model of consensus developed in close interaction with the industry and with the support of the European Commission. It is published as a technical specification by ETSI and provides an important contribution to achieve semantic interoperability for smart appliances. This paper builds on the success achieved in standardizing SAREF and presents SAREF4EE, an extension of SAREF. SAREF4EE has been created in collaboration with the EEBus and Energy@Home industry associations to interconnect their (different) data models. By using SAREF4EE, smart appliances from different manufacturers that support the EEBus or Energy@Home standards can easily communicate with each other using any energy management system at home or in the cloud."
  resource_du_14,14,"Heshan Du, Vania Dimitrova, Derek Magee, Anthony Cohn, Ross Stirling, Giulio Curioni, Barry Clarke and Helen Reeves",du,An Ontology of Soil Properties and Processes,resource,Ontologies (II),2,Heshan Du,paper_R50_.pdf,504+505,2016-10-21T13:30:00,2016-10-21T14:50:00,"Assessing the Underworld (ATU) is a large interdisciplinary UK research project addressing urban infrastructure challenges, especially how to make streetworks more efficient and sustainable. One of the key challenges it addresses is integrated inter-asset maintenance. As the assets on the surface of the ground (e.g. pavements) and those buried under it (e.g. pipes and cables) are supported by the ground, the properties and processes of soil affect the performance of these assets to a significant degree. In order to make integrated decisions, it is necessary to combine the knowledge and expertise in multiple areas, such as roads, soil, buried assets, sensing, etc. This requires an underpinning knowledge model, in the form of an ontology. Within this context, we present a new ontology for describing soil properties (e.g. soil strength) and processes (e.g. soil compaction), as well as how they affect each other. This ontology can be used to express how the ground affects and is affected by assets buried under the ground or on the ground surface. The ontology is written in OWL 2 and openly available from the University of Leeds data repository: http://doi.org/10.5518/54."
  resource_ermilov_4,4,"Ivan Ermilov, Jens Lehmann, Michael Martin and Sören Auer",ermilov,LODStats: The Data Web Census Dataset,resource,Linked Data Measurement,3,Ivan Ermilov,paper_R18_.pdf,504+505,2016-10-20T13:30:00,2016-10-20T14:50:00,"Over the past years, the size of the Data Web has increased significantly, which makes obtaining general insights into its growth and structure both more challenging and more desirable. The lack of such insights hinders important data management tasks such as quality, privacy and coverage analysis. In this paper, we present LODStats, which provides a comprehensive picture of the current state of a significant part of the Data Web. LODStats integrates RDF datasets from data.gov, publicdata.eu and datahub.io data catalogs and at the time of writing lists over 9 000 RDF datasets. For each RDF dataset, LODStats collects comprehensive statistics and makes these available in adhering to the LDSO vocabulary. This analysis has been regularly published and enhanced over the past four years at the public platform lodstats.aksw.org. We give a comprehensive overview over the resulting dataset."
  resource_faralli_10,10,"Stefano Faralli, Alexander Panchenko, Chris Biemann and Simone P. Ponzetto",faralli,Linked Disambiguated Distributional Semantic Networks,resource,Natural Language Processing,1,Stefano Faralli,paper_R37_.pdf,504+505,2016-10-21T10:30:00,2016-10-21T11:50:00,"We present a new hybrid knowledge base that combines the contextual information of distributional models with the conciseness and precision of manually constructed lexical networks. In contrast to dense vector representations, our resource is human readable and interpretable, and can be easily embedded within the Semantic Web ecosystem. Manual evaluation based on human judgments and an extrinsic evaluation on the task of Word Sense Disambiguation both indicate the high quality of the resource, as well as the benefits of enriching top-down lexical knowledge resources with bottom-up distributional information from text."
  resource_garcia_7,7,"Roberto García, Rosa Gil, Juan Manuel Gimeno, Eirik Bakke and David R. Karger",garcia,BESDUI: A Benchmark for End-User Structured Data User Interfaces,resource,Interaction,2,Roberto García,paper_R27_.pdf,504+505,2016-10-19T11:00:00,2016-10-19T12:20:00,"The Semantic Web Community has invested significant research effort in developing systems for Semantic Web search and exploration. But while it has been easy to assess the systems' computational efficiency, it has been much harder to assess how well different semantic systems help their users find and browse information. In this article, we propose and demonstrate the use of a benchmark for evaluating them, similar to the TREC benchmark for evaluating traditional search engines. Our benchmark includes a set of typical user tasks and a well-defined procedure for assigning a measure of performance on those tasks to a semantic system. We demonstrate its application to one such system, Rhizomer. We intend for this work to initiate a community conversation that will lead to a general accepted framework for comparing systems and measuring, and thus encouraging, progress towards better semantic search and exploration tools."
  resource_giovanni_6,6,"Andrea Giovanni Nuzzolese, Anna Lisa Gentile, Valentina Presutti and Aldo Gangemi",giovanni,Conference Linked Data: the ScholarlyData project,resource,Linked Data,1,Andrea Giovanni Nuzzolese,paper_R25_.pdf,504+505,2016-10-20T10:30:00,2016-10-20T11:50:00,"The Semantic Web Dog Food (SWDF) is the reference linked dataset of the Semantic Web community about papers, people, organisations, and events related to its academic conferences. In this paper we analyse the existing problems of generating, representing and maintaining Linked Data for the SWDF. With this work (i) we provide a refactored and cleaned SWDF dataset; (ii) we use a novel data model which improves the Semantic Web Conference Ontology, adopting best ontology design practices and (iii) we provide an open source maintenance workflow to support a healthy grow of the dataset beyond the Semantic Web conferences."
  resource_graux_12,12,"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,paper_R46_.pdf,501,2016-10-20T10:30:00,2016-10-20T11:50:00,"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_hernandez_20,20,"Daniel Hernandez, Aidan Hogan, Cristian Riveros, Carlos Rojas and Enzo Zerega",hernandez,"Querying Wikidata: Comparing SPARQL, Relational and Graph Databases",resource,Querying/SPARQL (I),3,Daniel Hernandez,paper_R69_.pdf,501,2016-10-20T10:30:00,2016-10-20T11:50:00,"In this paper, we experimentally compare the efficiency of various database engines for the purposes of querying the Wikidata knowledge-base, which can be conceptualised as a directed edge-labelled where edges can be annotated with meta-information called qualifiers. We select two popular SPARQL databases (Virtuoso, Blazegraph), a popular relational database (PostgreSQL), and a popular graph database (Neo4J) for comparison and discuss various options as to how Wikidata can be represented in the models of each engine. We design a set of experiments to test the relative query performance of these representations in the context of their respective engines. We first execute a large set of atomic lookups to establish a baseline performance for each test setting, and subsequently perform experiments on instances of more complex graph patterns based on real-world examples. We conclude with a summary of the strengths and limitations of the engines observed."
  resource_hu_5,5,"Wei Hu, Haoxuan Li, Zequn Sun, Xinqi Qian, Lingkun Xue, Ermei Cao and Yuzhong Qu",hu,Clinga: Bringing Chinese Physical and Human Geography in Linked Open Data,resource,Linked Data,4,Wei Hu,paper_R23_.pdf,504+505,2016-10-20T10:30:00,2016-10-20T11:50:00,"While the geographical domain has long been involved as an important part of the Linked Data, the small amount of Chinese linked geographical data hinders the integration and sharing of both Chinese and cross-lingual knowledge. In this paper, we contribute to the development of a new Chinese linked geographical dataset named Clinga, by obtaining data from the largest Chinese wiki encyclopedia. We manually design a new geography ontology to categorize a wide range of physical and human geographical entities, and carry out an automatic discovery of links to existing knowledge bases. The resulted Clinga dataset contains over half million Chinese geographical entities and is open access."
  resource_joshi_21,21,"Amit Joshi, Pascal Hitzler and Guozhu Dong",joshi,LinkGen: Multipurpose Linked Data Generator,resource,Linked Data,3,Amit Joshi,paper_R73_.pdf,504+505,2016-10-20T10:30:00,2016-10-20T11:50:00,"The paper presents a synthetic linked data generator that can generate a large amount of RDF data based on certain statistical distribution. Data generation is platform independent, supports streaming mode and produces output in N-Triples and N-Quad format. Different sets of output can be generated using various configuration parameters and the outputs are reproducible. Unlike existing generators, our generator accepts any vocabulary and can supplement the output with noisy and inconsistent data. The generator has an option to inter-link instances with real ones provided that the user supplies entities from real datasets."
  resource_link_24,24,"Vincent Link, Steffen Lohmann and Florian Haag",link,OntoBench: Generating Custom OWL 2 Benchmark Ontologies,resource,Reasoning,4,Vincent Link,paper_R88_.pdf,504+505,2016-10-21T15:30:00,2016-10-21T16:50:00,"A variety of tools for visualizing, editing, and documenting OWL ontologies have been developed in the last couple of years. The OWL coverage and conformance of these tools usually needs to be tested during development or for evaluation and comparison purposes. However, in particular for the testing of special OWL concepts and concept combinations, it can be tedious to find suitable ontologies and test cases. We have developed OntoBench, a generator for OWL 2 benchmark ontologies that can be used to test and compare ontology visualizers and related tools. In contrast to existing OWL benchmarks, OntoBench does not focus on scalability and performance but OWL coverage and concept combinations. Consistent benchmark ontologies are dynamically generated based on OWL 2 language constructs selected in a graphical user interface. OntoBench is available on GitHub and as a public service, making it easy to use the tool and generate custom ontologies or ontology fragments."
  resource_loseto_13,13,"Giuseppe Loseto, Saverio Ieva, Filippo Gramegna, Michele Ruta, Floriano Scioscia and Eugenio Di Sciascio",loseto,Linked Data (in low-resource) Platforms: a mapping for Constrained Application Protocol,resource,Knowledge Graphs,4,Giuseppe Loseto,paper_R48_.pdf,501,2016-10-19T11:00:00,2016-10-19T12:20:00,"This paper proposes a mapping of the Linked Data Platform (LDP) specification for Constrained Application Protocol (CoAP). Main motivation stems from the fact that LDP W3C Recommendation presents resource management primitives for HTTP only. Hence, use cases related to Web of Things scenarios, where HTTP-based communication and infrastructures are unfeasible, are partially neglected. A general translation of LDP-HTTP requests and responses is provided, as well as a fully comprehensive framework for HTTP-to-CoAP proxying. The theoretical work is corroborated by an experimental campaign using the W3C Test Suite for LDP."
  resource_mauri_18,18,"Andrea Mauri, Jean-Paul Calbimonte, Daniele Dell'Aglio, Marco Balduini, Marco Brambilla, Emanuele Della Valle and Karl Aberer",mauri,TripleWave: Spreading RDF Streams on the Web,resource,Streams,4,Andrea Mauri,paper_R62_.pdf,501,2016-10-20T13:30:00,2016-10-20T14:50:00,"Processing data streams is increasingly gaining a momentum, given the need to process these flows of information in real time and at Web scale. 
  In this context, RDF Stream Processing (RSP) and Stream Reasoning (SR) have emerged as solutions to combine semantic technologies with stream and event processing techniques. 
  Research in these areas has proposed an ecosystem of solutions to query, reason and perform real time processing over heterogeneous and distributed data streams on the Web.
  However, so far one basic building block has been missing: a mechanism to disseminate and exchange RDF streams on the Web.
  In this work we close this gap, proposing TripleWave, a reusable and generic tool that enables the publication of RDF streams on the Web. 
  The features of TripleWave have been derived from requirements of real use-cases, and consider a diverse set of scenarios, independent of any specific RSP implementation.
  TripleWave can be fed with existing Web streams (e.g. Twitter and Wikipedia streams) or time-annotated RDF datasets (e.g. the LinkedSensorData set), and it can be invoked through both pull- and push-based mechanisms, thus also enabling RSP engines to automatically register and receive data from TripleWave."
  resource_parsia_19,19,"Bijan Parsia, Nicolas Matentzoglu, Rafael S. Gonçalves, Birte Glimm and Andreas Steigmiller",parsia,The OWL Reasoner Evaluation (ORE) 2015 Resources,resource,Reasoning,3,Bijan Parsia,paper_R65_.pdf,504+505,2016-10-21T15:30:00,2016-10-21T16:50:00,"The OWL Reasoner Evaluation (ORE) Competition is an annual competition (with an associated workshop) which pits OWL 2 compliant reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we outline the design of the competition and present the infrastructure used for its execution: the corpora of ontologies, the competition framework, and the submitted systems. All resources are publicly available on the Web, allowing users to easily re-run the 2015 competition, or reuse any of the ORE infrastructure for reasoner experiments or ontology analysis."
  resource_peroni_8,8,"Silvio Peroni, Giorgia Lodi, Luigi Asprino, Aldo Gangemi and Valentina Presutti",peroni,FOOD: FOod in Open Data,resource,Linked Data,2,Silvio Peroni,paper_R28_.pdf,504+505,2016-10-20T10:30:00,2016-10-20T11:50:00,"This paper describes the outcome of an e-government project named FOOD, FOod in Open Data, which was carried out in the context of a collaboration between the Institute of Cognitive Sciences and Technologies of the Italian National Research Council, the Italian Ministry of Agriculture (MIPAAF) and the Italian Digital Agency (AgID). In particular, we implemented several ontologies for describing protected names of products (wine, pasta, fish, oil, etc.). In addition, we present the process carried out for producing and publishing a LOD dataset containing data extracted from existing Italian policy documents on such products and compliant with the aforementioned ontologies."
  resource_rebele_2,2,"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,paper_R4_.pdf,501,2016-10-19T14:00:00,2016-10-19T15:20:00,"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."
  resource_ristoski_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,paper_R1_.pdf,502,2016-10-20T15:30:00,2016-10-20T16:50:00,"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."
  resource_solanki_11,11,"Monika Solanki, Bojan Božić, Markus Freudenberg, Dimitris Kontokostas, Christian Dirschl and Rob Brennan",solanki,Enabling combined Software and Data engineering at Web-scale: The ALIGNED suite of Ontologies,resource,Ontologies (II),1,Monika Solanki,paper_R45_.pdf,504+505,2016-10-21T13:30:00,2016-10-21T14:50:00,"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."
  resource_sygkounas_22,22,"Efstratios Sygkounas, Giuseppe Rizzo and Raphaël Troncy",sygkounas,A Replication Study of the Top Performing Systems in SemEval Twitter Sentiment Analysis,resource,Natural Language Processing,3,Efstratios Sygkounas,paper_R81_.pdf,504+505,2016-10-21T10:30:00,2016-10-21T11:50:00,"We performed a thorough replicate study of the top systems performing in the yearly SemEval Twitter Sentiment Analysis task. We highlight some differences between the results obtained by the top systems and the ones we are able to compute. We also propose SentiME, an ensemble system composed of 5 state-of-the-art sentiment classifiers. SentiME first trains the different classifiers using the Bootstrap Aggregating Algorithm. The classification results are then aggregated using a linear function that averages the classification distributions of the different classifiers. SentiME has also been tested over the SemEval2015 test set, properly trained with the SemEval2015 train test, outperforming the best ranked system of the challenge. "
  resource_tonon_23,23,"Alberto Tonon, Victor Felder, Djellel Eddine Difallah and Philippe Cudré-Mauroux",tonon,VoldemortKG: Mapping schema.org and Web Entities to Linked Open Data,resource,Knowledge Graphs,1,Alberto Tonon,paper_R87_.pdf,501,2016-10-19T11:00:00,2016-10-19T12:20:00,"Increasingly, Web pages mix entities coming from different sources and represented in several different ways. It can thus happen that the same entity is both described by using schema.org annotations and by creating a text anchor pointing to its Wikipedia page. Often, those representations provide complementary information which is not exploited since those entities are disjoint.
  
  In this project, we explore the extent to which entities represented in different ways repeat on the Web, how they are related, and how they complement (or link) to each other. Our initial experiments show that we can unveil a previously (unexploited) knowledge graph by applying simple instance matching techniques on a large collection of schema.org annotations and DBpedia. The resulting knowledge graph aggregates entities (often tail entities) scattered across several Web pages, and complements existing DBpedia entities with new facts and properties.
  
  In order to facilitate further investigations in how to mine such information, we are releasing i) an excerpt of all CommonCrawl web pages containing both Wikipedia and schema.org annotations, ii) the toolset to extract this information and perform knowledge graph construction and mapping onto DBpedia, as well as iii) the resulting knowledge graph (VoldemortKG) obtained via label matching techniques."
  resource_wang_9,9,"Haofen Wang, Zhijia Fang, Jorge Gracia, Julia Bosque-Gil and Tong Ruan",wang,Zhishi.lemon：On Publishing Zhishi.me as Linguistic Linked Open Data,resource,Multilinguality,3,Haofen Wang,paper_R36_.pdf,501,2016-10-19T14:00:00,2016-10-19T15:20:00,"Recently, a growing number of linguistic resources in different languages have been published and interlinked as part of the Linguistic Linked Open Data (LLOD) cloud. However, in comparison to English and other prominent languages, the presence of Chinese in such a cloud is still limited, despite the fact that Chinese is the most spoken language worldwide. Publishing more Chinese language resources in the LLOD cloud can benefit both academia and industry to better understand the language itself and to further build multilingual applications that will improve the flow of data and services across countries. In this paper, we describe Zhishi.lemon, a newly developed dataset based on the lemon model that constitutes the lexical realization of Zhishi.me, one of the largest Chinese datasets in the Linked Open Data (LOD) cloud. Zhishi.lemon combines the lemon core with the lemon translation module in order to build a linked data lexicon in Chinese with translations into Spanish and English. Links to BabelNet (a vast multilingual encyclopedic resource) have been provided as well. We also present a showcase of this module along with the technical details of transforming Zhishi.me to Zhishi.lemon. We have made the dataset accessible on the Web for both humans (via a Web interface) and software agents (with a SPARQL endpoint)."
  resource_wilmering_17,17,"Thomas Wilmering, György Fazekas and Mark B. Sandler",wilmering,AUFX-O: Novel Methods for the Representation of Audio Processing Workflows,resource,Ontologies (II),4,Thomas Wilmering,paper_R60_.pdf,504+505,2016-10-21T13:30:00,2016-10-21T14:50:00,"This paper introduces the Audio Effects Ontology (AUFX-O) building on previous theoretical models describing audio processing units and workflows in the context of music production. We discuss important conceptualisations of different abstraction layers, their necessity to successfully model audio effects, and their application method. We present use cases concerning the application of effects in music production projects, and the creation of audio effect metadata facilitating a linked data service exposing information about effect implementations. By doing so, we show how our model benefits knowledge sharing, and enables reproducibility and analysis of audio production workflows."
  