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 âÂÂanytimeâ behaviour. We present scalability results and next steps for improvement.
Abstract: We present a technique for answering queries over RDF data through an evolutionary search algorithm, using fingerprinting and Bloom filters for rapid approximate evaluation of generated solutions. Our evolutionary approach has several advantages compared to traditional database-style query answering. First, the result quality increases monotonically and converges with each evolution, offering âÂÂanytimeâ behaviour with arbitrary trade-off between computation time and query results; in addition, the level of approximation can be tuned by varying the size of the Bloom filters. Secondly, through Bloom filter compression we can fit large graphs in main memory, reducing the need for disk I/O during query evaluation. Finally, since the individuals evolve independently, parallel execution is straightforward. We present our prototype that evaluates basic SPARQL queries over arbitrary RDF graphs and show initial results over large datasets.
