Building an effective and efficient background knowledge resource to enhance ontology matching

International audienceOntology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1)~a selection based on a set of rules and (2)~a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F-measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources

Une méthode de detection et modélisation d'événements des messages sur Twitter

IRSTEA PUB00045753International audienceThis paper introduces TEWS —Twitter Events on the Semantic Web, pronounced like " news " —a semantic web tool for detection and representation of events taking as an input the social stream Twitter. The tool assists the user throughout a complete processing chain, starting from the detection of events on Twitter, their modeling and representation following the semantic web principles, to their storing in an RDF knowledge base that can be further published on the Web of Data

Extended Tversky Similarity for Resolving Terminological Heterogeneities across Ontologies

International audienceWe propose a novel method to compute similarity between cross-ontology concepts based on the amount of overlap of the information content of their labels. We extend Tversky's similarity measure by using the information content of each term within an ontology label both for the similarity computation and for the weight assignment to tokens. The approach is suitable for handling compound labels. Our experiments showed that it outperforms existing terminological similarity measures for the ontology matching task

View Adaptation in Fragment-Based Approach

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Data Integration Over the Web

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Schema Evolution in Data Warehouses

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View Selection and Materialization

International audienceThere are many motivations for investigating the view selection problem. At first, materialized views are increasingly being supported by commercial database systems and are used to speed up query response time. Therefore, the problem of choosing an appropriate set of views to materialize in the database is crucial in order to improve query processing cost. Another application of the view selection issue is selecting views to materialize in data warehousing systems to answer decision support queries. The problem addressed in this paper is similar to that of deciding which views to materialize in data warehousing. However, most existing view selection methods are static. Moreover, none of these methods have considered the problem of de-materializing the already materialized views. Yet it is a very important issue since the size of storage space is usually restricted. This chapter deals with the problem of dynamic view selection and with the pending issue of removing materialized views in order to replace less beneficial views with more beneficial views. We propose a view selection method for deciding which views to materialize according to statistic metadata. More precisely, we have designed and implemented our view selection method, including a polynomial algorithm, to decide which views to materialize

Maintenance de Vues XML Stockées dans un SGBD Relationnel

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