Query optimization for ontology-based information integration
Yingjie Li, Jeff Heflin · 2010
In recent years, there has been an explosion of publicly avail-able RDF and OWL data sources. In order to effectively and quickly answer queries in such an environment, we present an approach to identifying the potentially relevant Semantic Web data sources using query rewritings and a term index. We demonstrate that such an approach must carefully han-dle query goals that lack constants; otherwise the algorithm may identify many sources that do not contribute to even-tual answers. This is because the term index only indicates if URIs are present in a document, and specific answers to a subgoal cannot be calculated until the source is physi-cally accessed- an expensive operation given disk/network latency. We present an algorithm that, given a set of query rewritings that accounts for ontology heterogeneity, incre-mentally selects and processes sources in order to maintain selectivity. Once sources are selected, we use an OWL rea-soner to answer queries over these sources and their corre-sponding ontologies. We present the results of experiments using both a synthetic data set and a subset of the real-world Billion Triple Challenge data.