Matches in ScholarlyData for { ?s ?p ?o. }
- 246 label "Distance Based clustering of Semantic Web Resources".
- 246 sameAs 246.
- 246 abstract "The original Semantic Web vision was explicit in the need for intelligent autonomous agents that would represent users and help them navigate the Semantic Web. We argue that an essential feature for such agents is the capability to analyse data and learn. In this paper we outline the challenges and issues surrounding the application of clustering algorithms to Semantic Web data. We present several ways to extract instances from a large RDF graph and computing the distance between these. We evaluate our approaches on three different data-sets, one representing a typical relational database to RDF conversion, one based on data from a ontologically rich Semantic Web enabled application, and one consisting of a crawl of FOAF documents; applying both supervised and unsupervised evaluation metrics. Our evaluation did not support choosing a single combination of instance extraction method and similarity metric as superior in all cases, and as expected the behaviour depends greatly on the data being clustered. Instead, we attempt to identify characteristics of data that make particular methods more suitable.".
- 246 hasAuthorList authorList.
- 246 hasTopic Machine_learning.
- 246 hasTopic Personalization.
- 246 hasTopic Semantic_Web.
- 246 isPartOf proceedings.
- 246 keyword "clustering".
- 246 keyword "distance measure".
- 246 keyword "rdf".
- 246 title "Distance Based clustering of Semantic Web Resources".
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- item-1 hasContent gunnar-aastrand-grimnes.
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- item-2 hasContent peter-edwards.
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- item-3 type ListItem.
- item-3 hasContent alun-preece.
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- 252 creator claudia-damato.
- 252 creator floriana-esposito.
- 252 creator nicola-fanizzi.
- 252 type InProceedings.
- 252 label "Query Answering and Ontology Population: an Inductive Approach".
- 252 sameAs 252.
- 252 abstract "In the context of Semantic Web, deductive reasoning is used for making explicit the implicit knowledge of a knowledge base (KB). Anyway, purely logic-based approaches can fail when data comes from distributed sources, where contradictions usually turn out. Inductive instance-based learning methods can be effectively used in such a case, since they are well known to be efficient and fault tolerant. In this paper we propose an inductive method for improving the concept retrieval and for the performing the ontology population in a (semi-)automatic way. By casting concept retrieval to a classification problem with the goal of assessing the individual memberships w.r.t. the query concepts, we propose an extension of the \emph{k-Nearest Neighbor} algorithm for Description Logic KBs. It is based on the exploitation of an \emph{entropy}-based dissimilarity measure. The procedure retrieves individuals belonging to query concepts, by analogy with other training instances, on the grounds of the classification of the nearest ones w.r.t.\ the dissimilarity measure. We experimentally show that the behavior of the classifier is comparable with the one of a standard reasoner. Moreover we show that new knowledge (not logically derivable) is induced. It can be suggested to the knowledge engineer for validation, during the ontology population task.".
- 252 hasAuthorList authorList.
- 252 hasTopic Formal_languages.
- 252 hasTopic Inference.
- 252 hasTopic Logic.
- 252 hasTopic Machine_learning.
- 252 hasTopic Search_engine.
- 252 hasTopic Semantic_Web.
- 252 hasTopic Web_2.0.
- 252 isPartOf proceedings.
- 252 keyword "description logic".
- 252 keyword "inductive learning".
- 252 keyword "ontology population and query unswering".
- 252 keyword "similalrity measure".
- 252 keyword "uncertainty".
- 252 title "Query Answering and Ontology Population: an Inductive Approach".
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- item-1 hasContent claudia-damato.
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- item-2 hasContent nicola-fanizzi.
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- item-3 type ListItem.
- item-3 hasContent floriana-esposito.
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- 260 creator anthony-ventresque.
- 260 creator patrick-valduriez.
- 260 creator philippe-lamarre.
- 260 creator sylvie-cazalens.
- 260 type InProceedings.
- 260 label "Improving interoperability using query interpretation in semantic vector spaces".
- 260 sameAs 260.
- 260 abstract "In semantic web applications where query initiators and information providers do not necessarily share the same ontology, semantic interoperability typically relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the ``shared'' parts of the ontologies) but, in some sense, limited to them. Then, an important question which has not received attention is how the ``unshared'' parts can also contribute to and improve information exchange. In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator's side and a query interpretation step at the document provider's. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared.".
- 260 hasAuthorList authorList.
- 260 hasTopic Data_integration.
- 260 hasTopic SPARQL.
- 260 hasTopic Semantic_Web.
- 260 isPartOf proceedings.
- 260 keyword "query expansion".
- 260 keyword "query interpretation".
- 260 keyword "semantic interoperability".
- 260 keyword "semantic vectors".
- 260 title "Improving interoperability using query interpretation in semantic vector spaces".
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- item-1 hasContent anthony-ventresque.
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- item-2 hasContent sylvie-cazalens.
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- item-3 hasContent philippe-lamarre.