In order to manage the increasing amount of RDF data, an RDF repository should provide not only necessary scalability and efficiency, but also sufficient inference capabilities. Though existing RDF repositories have made progress towards this goal, there is still ample space for improving the overall performance. In this paper, we propose a native RDF repository, System II, to pursue a better tradeoff among the system scalability, the query efficiency, and the inference capabilities. System II takes the hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. Based on this native storage scheme, a set of efficient semantic query processing techniques are designed. First, several indices are built to accelerate RDF data access including a value index, a labeling scheme for transitive closure computation, and three triple indices. Second, we propose a hybrid inference strategy under the pD* semantics to support inference for OWL-Lite with a relatively low computational complexity. Finally, we extend the SPARQL algebra to explicitly express inference semantics in logical query plan by defining new algebra operators. The results of performance evaluation on the LUBM benchmark show that System II has a better combined metric value than the other comparable systems.

To manage the increasing amount of RDF data, an RDF repository should provide not only necessary scalability and efficiency, but also sufficient inference capabilities. In this paper, we propose a native RDF repository, System, to pursue a better tradeoff among the above requirements. System takes the hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. In addition, a set of efficient semantic query processing techniques are designed. The results of performance evaluation on the LUBM benchmark show that System has a better combined metric value than the other comparable systems.

Addressed in this paper is the issue of semantic relationship extraction from semi-structured documents. Many research efforts have been made so far on the semantic information extraction. However, much of the previous work focuses on detecting `isolated' semantic information by making use of linguistic analysis or linkage information in web pages and limited research has been done on extracting semantic relationship from the semi-structured documents. In this paper, we propose a method for semantic relationship extraction by using the logical information in the semi-structured document (semi-structured document usually has various types of structure information, e.g. a semi-structured document may be hierarchical laid out). To the best of our knowledge, extracting semantic relationships by using logical information has not been investigated previously. A probabilistic approach has been proposed in the paper. Features used in the probabilistic model have been defined.