The Experts below are selected from a list of 7395 Experts worldwide ranked by ideXlab platform
Marco Manna - One of the best experts on this subject based on the ideXlab platform.
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DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries
Theory and Practice of Logic Programming, 2020Co-Authors: Alessio Fiorentino, Jessica Zangari, Marco MannaAbstract:AbstractThe W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications. Since its unrestricted usage makes reasoning undecidable already in case of very simple tasks, expressive yet decidable fragments have been identified. Among them, we focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources - such as DBpedia - fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting query answering and SPARQL queries; (iii) properly applying the sameAs property without adopting the unique Name Assumption; (iv) dealing with concrete datatypes. To fill the gap, we present DaRLing, a freely available Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In particular, we describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability.
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DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries
arXiv: Artificial Intelligence, 2020Co-Authors: Alessio Fiorentino, Jessica Zangari, Marco MannaAbstract:The W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications. Since its unrestricted usage makes reasoning undecidable already in case of very simple tasks, expressive yet decidable fragments have been identified. Among them, we focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources - such as DBpedia - fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting query answering and SPARQL queries; (iii) properly applying the sameAs property without adopting the unique Name Assumption; (iv) dealing with concrete datatypes. To fill the gap, we present DaRLing, a freely available Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In particular, we describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Dmitry Tishkovsky - One of the best experts on this subject based on the ideXlab platform.
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a refined tableau calculus with controlled blocking for the description logic shoi
International Workshop Description Logics, 2013Co-Authors: Mohammad Khodadadi, Renate A Schmidt, Dmitry TishkovskyAbstract:The paper presents a tableau calculus with several refinements for reasoning in the description logic \(\mathcal{SHOI}\). The calculus uses non-standard rules for dealing with TBox statements. Whereas in existing tableau approaches a fixed rule is used for dealing with TBox statements, we use a dynamically generated set of refined rules. This approach has become practical because reasoners with flexible sets of rules can be generated with the tableau prover generation prototype MetTel. We also define and investigate variations of the unrestricted blocking mechanism in which equality reasoning is realised by ordered rewriting and the application of the blocking rule is controlled by excluding its application to a fixed, finite set of individual terms. Reasoning with the unique Name Assumption and excluding ABox individuals from the application of blocking can be seen as two separate instances of the latter. Experiments show the refinements lead to fewer rule applications and improved performance.
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Description Logics - A Refined Tableau Calculus with Controlled Blocking for the Description Logic SHOI
Lecture Notes in Computer Science, 2013Co-Authors: Mohammad Khodadadi, Renate A Schmidt, Dmitry TishkovskyAbstract:The paper presents a tableau calculus with several refinements for reasoning in the description logic \(\mathcal{SHOI}\). The calculus uses non-standard rules for dealing with TBox statements. Whereas in existing tableau approaches a fixed rule is used for dealing with TBox statements, we use a dynamically generated set of refined rules. This approach has become practical because reasoners with flexible sets of rules can be generated with the tableau prover generation prototype MetTel. We also define and investigate variations of the unrestricted blocking mechanism in which equality reasoning is realised by ordered rewriting and the application of the blocking rule is controlled by excluding its application to a fixed, finite set of individual terms. Reasoning with the unique Name Assumption and excluding ABox individuals from the application of blocking can be seen as two separate instances of the latter. Experiments show the refinements lead to fewer rule applications and improved performance.
Danai Symeonidou - One of the best experts on this subject based on the ideXlab platform.
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Automatic key discovery for Data Linking
2014Co-Authors: Danai SymeonidouAbstract:In the recent years, the Web of Data has increased significantly, containing a huge number of RDF triples. Integrating data described in different RDF datasets and creating semantic links among them, has become one of the most important goals of RDF applications. These links express semantic correspondences between ontology entities or data. Among the different kinds of semantic links that can be established, identity links express that different resources refer to the same real world entity. By comparing the number of resources published on the Web with the number of identity links, one can observe that the goal of building a Web of data is still not accomplished. Several data linking approaches infer identity links using keys. Nevertheless, in most datasets published on the Web, the keys are not available and it can be difficult, even for an expert, to declare them. The aim of this thesis is to study the problem of automatic key discovery in RDF data and to propose new efficient approaches to tackle this problem. Data published on the Web are usually created automatically, thus may contain erroneous information, duplicates or may be incomplete. Therefore, we focus on developing key discovery approaches that can handle datasets with numerous, incomplete or erroneous information. Our objective is to discover as many keys as possible, even ones that are valid in subparts of the data. We first introduce KD2R, an approach that allows the automatic discovery of composite keys in RDF datasets that may conform to different schemas. KD2R is able to treat datasets that may be incomplete and for which the Unique Name Assumption is fulfilled. To deal with the incompleteness of data, KD2R proposes two heuristics that offer different interpretations for the absence of data. KD2R uses pruning techniques to reduce the search space. However, this approach is overwhelmed by the huge amount of data found on the Web. Thus, we present our second approach, SAKey, which is able to scale in very large datasets by using effective filtering and pruning techniques. Moreover, SAKey is capable of discovering keys in datasets where erroneous data or duplicates may exist. More precisely, the notion of almost keys is proposed to describe sets of properties that are not keys due to few exceptions.
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Discovering keys in RDF/OWL dataset with KD2R
2013Co-Authors: Danai Symeonidou, Nathalie Pernelle, Fatiha SaïsAbstract:KD2R allows the automatic discovery of composite key constraints in RDF data sources that conform to a given ontol-ogy. We consider data sources for which the Unique Name Assumption is fulfilled. KD2R allows this discovery without having to scan all the data. Indeed, the proposed system looks for maximal non keys and derives minimal keys from this set of non keys. KD2R has been tested on several datasets available on the web of data and it has obtained promising results when the discovered keys are used to link data. In the demo, we will demonstrate the functionality of our tool and we will show on several datasets that the keys can be used in a datalinking tool.
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WOD - Discovering keys in RDF/OWL dataset with KD2R
Proceedings of the 2nd International Workshop on Open Data - WOD '13, 2013Co-Authors: Danai Symeonidou, Nathalie Pernelle, Fatiha SaïsAbstract:KD2R allows the automatic discovery of composite key constraints in RDF data sources that conform to a given ontology. We consider data sources for which the Unique Name Assumption is fulfilled. KD2R allows this discovery without having to scan all the data. Indeed, the proposed system looks for maximal non keys and derives minimal keys from this set of non keys. KD2R has been tested on several datasets available on the web of data and it has obtained promising results when the discovered keys are used to link data. In the demo, we will demonstrate the functionality of our tool and we will show on several datasets that the keys can be used in a datalinking tool.
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An automatic key discovery approach for data linking
Journal of Web Semantics, 2013Co-Authors: Nathalie Pernelle, Fatiha Saïs, Danai SymeonidouAbstract:In the context of Linked Data, different kinds of semantic links can be established between data. However when data sources are huge, detecting such links manually is not feasible. One of the most important types of links, the identity link, expresses that different identifiers refer to the same real world entity. Some automatic data linking approaches use keys to infer identity links, nevertheless this kind of knowledge is rarely available. In this work we propose KD2R, an approach which allows the automatic discovery of composite keys in RDF data sources that may conform to different schemas. We only consider data sources for which the Unique Name Assumption is fulfilled. The obtained keys are correct with respect to the RDF data sources in which they are discovered. The proposed algorithm is scalable since it allows the key discovery without having to scan all the data. KD2R has been tested on real datasets of the international contest OAEI 2010 and on datasets available on the web of data, and has obtained promising results.
Alessio Fiorentino - One of the best experts on this subject based on the ideXlab platform.
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DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries
Theory and Practice of Logic Programming, 2020Co-Authors: Alessio Fiorentino, Jessica Zangari, Marco MannaAbstract:AbstractThe W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications. Since its unrestricted usage makes reasoning undecidable already in case of very simple tasks, expressive yet decidable fragments have been identified. Among them, we focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources - such as DBpedia - fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting query answering and SPARQL queries; (iii) properly applying the sameAs property without adopting the unique Name Assumption; (iv) dealing with concrete datatypes. To fill the gap, we present DaRLing, a freely available Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In particular, we describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability.
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DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries
arXiv: Artificial Intelligence, 2020Co-Authors: Alessio Fiorentino, Jessica Zangari, Marco MannaAbstract:The W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications. Since its unrestricted usage makes reasoning undecidable already in case of very simple tasks, expressive yet decidable fragments have been identified. Among them, we focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources - such as DBpedia - fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting query answering and SPARQL queries; (iii) properly applying the sameAs property without adopting the unique Name Assumption; (iv) dealing with concrete datatypes. To fill the gap, we present DaRLing, a freely available Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In particular, we describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).
Mohammad Khodadadi - One of the best experts on this subject based on the ideXlab platform.
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a refined tableau calculus with controlled blocking for the description logic shoi
International Workshop Description Logics, 2013Co-Authors: Mohammad Khodadadi, Renate A Schmidt, Dmitry TishkovskyAbstract:The paper presents a tableau calculus with several refinements for reasoning in the description logic \(\mathcal{SHOI}\). The calculus uses non-standard rules for dealing with TBox statements. Whereas in existing tableau approaches a fixed rule is used for dealing with TBox statements, we use a dynamically generated set of refined rules. This approach has become practical because reasoners with flexible sets of rules can be generated with the tableau prover generation prototype MetTel. We also define and investigate variations of the unrestricted blocking mechanism in which equality reasoning is realised by ordered rewriting and the application of the blocking rule is controlled by excluding its application to a fixed, finite set of individual terms. Reasoning with the unique Name Assumption and excluding ABox individuals from the application of blocking can be seen as two separate instances of the latter. Experiments show the refinements lead to fewer rule applications and improved performance.
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Description Logics - A Refined Tableau Calculus with Controlled Blocking for the Description Logic SHOI
Lecture Notes in Computer Science, 2013Co-Authors: Mohammad Khodadadi, Renate A Schmidt, Dmitry TishkovskyAbstract:The paper presents a tableau calculus with several refinements for reasoning in the description logic \(\mathcal{SHOI}\). The calculus uses non-standard rules for dealing with TBox statements. Whereas in existing tableau approaches a fixed rule is used for dealing with TBox statements, we use a dynamically generated set of refined rules. This approach has become practical because reasoners with flexible sets of rules can be generated with the tableau prover generation prototype MetTel. We also define and investigate variations of the unrestricted blocking mechanism in which equality reasoning is realised by ordered rewriting and the application of the blocking rule is controlled by excluding its application to a fixed, finite set of individual terms. Reasoning with the unique Name Assumption and excluding ABox individuals from the application of blocking can be seen as two separate instances of the latter. Experiments show the refinements lead to fewer rule applications and improved performance.
