With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The structural characteristics of RDF data graphs, predicate path sequence indices (PPS-indices), were used to efficiently prune the search space, which captured the inherent features of the RDF data graphs, while the database is updated. Our storage model was based on a relational database. Compared to a baseline solution, the proposed method effectively reduced the cardinalities of the intermediate results during the query processing, and at least an order of magnitude improvement is achieved in filtering performance, thereby improving the efficiency of the query execution.

With the development of the Semantic Web and information extraction technologies, more and more available RDF data has emerged on the Web. It has become one of the research hot spots in semantic Web technology. In this paper, in terms of RDF data organization and storage, as well as query expression, query processing. The analysis and comparison of RDF keyword query approaches are presented deeply and future research challenges are put forward.