anchor.diff:  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. 
  +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 
  +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."
  
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:   of complex subtasks, such as structure inference and matching of
    both instances and concepts, each of which gives rise to uncertain
    outcomes. Such uncertainty is unavoidable given the semantically heterogeneous
    nature of web sources, including LD ones. This paper approaches the problem of
    structuring LD search results as an evidence-based one. In particular, the paper shows
    how one formalism (viz., probabilistic soft logic (PSL)) can be exploited to assimilate
    different sources of evidence in a principled way and to beneficial
    effect for users. The paper considers syntactic evidence derived from matching
    algorithms, semantic evidence derived from LD vocabularies, and user
    evidence, in the form of feedback. The main contributions are: sets
    of PSL rules that model the uniform assimilation of diverse kinds of evidence,
    an empirical evaluation of how the resulting PSL programs perform in terms
    of their ability to infer structure in LD search results, and, finally, a concrete
    example of how populating such inferred structures for presentation
    to the end user is beneficial, besides enabling the collection of
    feedback whose assimilation further improves search result presentation."
  research_angles_33,33,Renzo Angles and Claudio Gutierrez,angles,The multiset semantics of SPARQL patterns,research,Querying,1,Renzo Angles ,paper_M232_.pdf,502,2016-10-19T14:00:00,2016-10-19T15:20:00,"The paper determines the algebraic and logic structure produced by  the multiset semantics of the core patterns of SPARQL. We prove that the fragment formed by AND, UNION, OPTIONAL, FILTER, MINUS and SELECT corresponds precisely to both, the intuitive multiset relational algebra (projection, selection, natural join, arithmetic union and except), and multiset classical non-recursive Datalog with safe negation."
  research_bereta_19,19,Konstantina Bereta and Manolis Koubarakis,bereta,Ontop of Geospatial Databases,research,Querying/SPARQL (II),4,Konstantina Bereta ,paper_M98_.pdf,502,2016-10-21T13:30:00,2016-10-21T14:50:00,"In this paper we propose an OBDA approach for accessing geospatial data stored in geospatial relational databases, using the OGC standard GeoSPARQL and R2RML or OBDA mappings. We introduce  extensions  to existing SPARQL-to-SQL approaches to support GeoSPARQL features. We describe the implementation of our approach in the system ontop-spatial, an extension of the OBDA system Ontop for creating  virtual geospatial RDF graphs on top of geospatial relational databases. Last, we present an experimental evaluation of our system using workload and queries from a  recent benchmark. In order to measure the performance of our system,  we compare it to the state-of-the-art geospatial RDF store, and confirm its efficiency."
  research_brasileiro_11,11,"Freddy Brasileiro, Joao Paulo Almeida, Victorio Albani Carvalho and Giancarlo Guizzardi",brasileiro,Expressive Multi-Level Modeling for the Semantic Web,research,Knowledge Representation,4,Freddy Brasileiro,paper_M177_.pdf,502,2016-10-19T11:00:00,2016-10-19T12:20:00,"The assessment of risk in medicine is a crucial task, depending on scientific knowledge derived by rigorous clinical studies regarding the (quantified) factors affecting biological changes, as well as on particular knowledge about the current status of a particular patient. Existing non-semantic risk prediction tools are typically based on hardcoded scientific knowledge, and only cover a very limited range of patient states. This makes them rapidly out of date, and limited in application, particularly for patients with co-morbidities (multiple co-occurring conditions). Semantic Web and Quantified Self technologies make it possible to address this task in a much more principled way, to maximise knowledge and data reuse and minimise maintenance requirements while enabling new and sophisticated applications involving widely-available biometric sensors.
  
  We present a framework for calculating clinical risk predictions for patients based on automatically-gathered biometric data. This framework relies on generic, reusable ontologies for representing clinical risk, and sensor readings, and reasoning to support the integration of data represented according to these ontologies. This integration makes novel use of Semantic Web technologies, and supports straightforward extension and maintenance by medical professionals. The framework is evaluated in terms of its predictions, extensibility and ease of use for domain experts."
  research_carral_6,6,"David Carral, Cristina Feier and Pascal Hitzler",carral,A Practical Acyclicity Notion for Query Answering over Horn-SRIQ Ontologies,research,Reasoning,1,David Carral,paper_M202_.pdf,503,2016-10-21T15:30:00,2016-10-21T16:50:00,"Conjunctive query answering over expressive Horn Description Logic ontologies is a relevant and challenging problem which, in some cases, can be addressed by application of the chase algorithm.
  