Abstract
RDF is increasingly being used to represent large amounts of data on the Web. Current query evaluation strategies for RDF are inspired by databases, assuming perfect answers on finite repositories. In this paper, we focus on a query method based on evolutionary computing, which allows us to handle uncertainty, incompleteness and unsatisfiability, and deal with large datasets, all within a single conceptual framework. Our technique supports approximate answers with "anytime" behaviour. We present scalability results and next steps for improvement. © 2008 Springer-Verlag.
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CITATION STYLE
Guéret, C., Oren, E., Schlobach, S., & Schut, M. (2008). An evolutionary perspective on approximate RDF query answering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5291 LNAI, pp. 215–228). https://doi.org/10.1007/978-3-540-87993-0_18
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