kk-nearest keyword search in RDF graphs

• We propose a novel and useful kk-nearest keyword (kk-NK) query over RDF data graphs.
• We design effective pruning strategies for kk-NK queries with and without schema.
• We present effective indexing mechanisms to greatly facilitate pruning strategies.
• We integrate our pruning and indexing methods into efficient kk-NK query procedure.
• We conduct extensive experiments to confirm the efficiency of our approaches.

Resource Description Framework (RDF) has been widely used as a W3C standard to describe the resource information in the Semantic Web. A standard SPARQL query over RDF data requires query issuers to fully understand the domain knowledge of the data. Because of this fact, SPARQL queries over RDF data are not flexible and it is difficult for non-experts to create queries without knowing the underlying data domain. Inspired by this problem, in this paper, we propose and tackle a novel and important query type, namely kk-nearest keyword   (kk-NK) query, over a large RDF graph. Specifically, a kk-NK query obtains kk closest pairs of vertices, (vi,ui)(vi,ui), in the RDF graph, that contain two given keywords qq and ww, respectively, such that uiui is the nearest vertex of vivi that contains the keyword ww. To efficiently answer kk-NK queries, we design effective pruning methods for RDF graphs both with and without schema, which can greatly reduce the query search space. Moreover, to facilitate our pruning strategies, we propose effective indexing mechanisms on RDF graphs with/without schema to enable fast kk-NK query answering. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed kk-NK query processing approaches.