Query Rewriting for an Incremental Search in Heterogeneous Linked Data Sources
Nowadays, the number of linked data sources available on the Web is considerable. In this scenario, users are interested in frameworks that help them to query those heterogeneous data sources in a friendly way, so avoiding awareness of the technical details related to the heterogeneity and variety of data sources. With this aim, we present a system that implements an innovative query approach that obtains results to user queries in an incremental way. It sequentially accesses different datasets, expressed with possibly different vocabularies. Our approach enriches previous answers each time a different dataset is accessed. Mapping axioms between datasets are used for rewriting the original query and so obtaining new queries expressed with terms in the vocabularies of the target dataset. These rewritten queries may be semantically equivalent or they could result in a certain semantic loss; in this case, an estimation of the loss of information incurred is presented.
KeywordsSemantic Web Linked Open Data Sources SPARQL query vocabulary mapping query rewriting
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