International audienceSPARQL query language plays a significant role in developing semantic web and web intelligence. In order to deal with large data query execution over RDF, SPARQL query optimizer is an essential component in the SPARQL query engine for improving query execution performance. This paper proposes an approach for performing query planning and optimization based on an extended query pattern graph and heuristics. First, this paper generalizes SPARQL query statement representation by taking other expressions into account, aiming at overcoming the limitations of only using basic query triple patterns. Second, this paper presents the heuristics for estimating the cost of executing query triple pattern. The proposed query planning methods are implemented within Corese query engine and are evaluated using BSBM benchmark. The results suggest that the proposed methods can optimize effectively the query execution time of SPARQL query engine
http://www-sop.inria.fr/members/Fuqi.Song/files/Pattern-based%20core%20word%20recognition%20to%20support%20ontology%20matching.%20Fuqi%20Song.pdfInternational audienceOntology matching is a crucial issue in the domain of semantic web and data interoperability. In this paper, a core word based method for measuring similarity from the semantic level of ontology entities is described. In ontology, most labels of entities are compound words rather than single meaningful words. However, the main meaning is represented usually by one word of them, which is called core word. The core word is learned by investigating certain patterns, which are defined based on part of speech (POS) and linguistics knowledge. The other information is noted as complementary information. An algorithm is given to measure the similarity between a pair of compound words and short texts. In order to support diverse situation, especially when core words cannot be recognized, non semantic based ontology matching techniques are applied from lexical and structural level of ontology. The described method is tested on real ontology and benchmarking data sets. It showed good matching ability and obtained promising results
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