Arun Viswanathan, Nancy Feldman, et al.
IEEE Communications Magazine
Keyword search is a useful tool for exploring large RDF data sets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summaries from the RDF graphs for query processing. In this work, we show that existing techniques have serious limitations in dealing with realistic, large RDF data with tens of millions of triples. Furthermore, the existing summarization techniques may lead to incorrect/incomplete results. To address these issues, we propose an effective summarization algorithm to summarize the RDF data. Given a keyword query, the summaries lend significant pruning powers to exploratory keyword search and result in much better efficiency compared to previous works. Unlike existing techniques, our search algorithms always return correct results. Besides, the summaries we built can be updated incrementally and efficiently. Experiments on both benchmark and large real RDF data sets show that our techniques are scalable and efficient.
Arun Viswanathan, Nancy Feldman, et al.
IEEE Communications Magazine
Frank R. Libsch, S.C. Lien
IBM J. Res. Dev
Beomseok Nam, Henrique Andrade, et al.
ACM/IEEE SC 2006
Michael C. McCord, Violetta Cavalli-Sforza
ACL 2007