Scaling concurrency of personalized Semantic search over Large RDF data

Recent keyword search techniques on Semantic Web are moving away from shallow, information retrieval-style approaches that merely find "keyword matches" towards more interpretive approaches that attempt to induce structure from keyword queries. The process of query interpretation is usually guided by structures in data, and schema and is often supported by a graph exploration procedure. However, graph exploration-based interpretive techniques are impractical for multi-tenant scenarios for large databases because separate expensive graph exploration states need to be maintained for different user queries. This leads to significant memory overhead in situations of large numbers of concurrent requests. This limitation could negatively impact the possibility of achieving the ultimate goal of personalizing search. In this paper, we propose a lightweight interpretation approach that employs indexing to improve throughput and concurrency with much less memory overhead. It is also more amenable to distributed or partitioned execution. The approach is implemented in a system called "SKI" and an experimental evaluation of SKI's performance on the DBPedia and Billion Triple Challenge datasets shows orders-of-magnitude performance improvement over existing techniques.