To obtain comparable high query performance with relational databases, diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework (RDF) data and SPARQL query. Database caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/temporal/semantic locality principle. However, existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query semantics. Although semantic caching approaches work effectively in this case, little work has been done in this area. In this paper, we try to improve SPARQL query performance with semantic caching approaches, i.e., SPARQL algebraic expression tree (AET) based caching and entity caching. Successive queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two approaches. The approaches are implemented on a two-level-storage structure. The main memory stores the most frequently accessed cache items, and items swapped out are stored on the disk for future possible reuse. Evaluation results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our approaches. Comparisons with previous research are also provided.

Falconer is a semantic Web search engine enhanced SIOC (Semantically-Interlinked Online Communities) application, which is designed to demonstrate the ability of accelerating the creation and reuse process of semantic Web data with easy-to-use user interfaces. In this process, semantic Web search engines feed existing semantic data into the SIOC framework, where new semantic data are composed by the community and indexed again by those search engines. Compared to existing social (semantic) Web applications, Falconer inherently conforms to SIOC specification. It provides semantic search engine based user registration suggestion, friends auto-discovery, and semantic annotation for forum post content. Another distinctive feature is that it enables users to subscribe any resource having a URI as the topic they are interested in. The relationships among users, topics, and posts are further visualized for analyzing the topic trends in the community. As all semantic data are formatted in RDF and RDFa, they can be queried with SPARQL query language.