Managing and Refining Rule Set for SWRL

Reasoning rules play a primary role in the reasoning process to retrieve useful knowledge on the Semantic Web. The Semantic Web Rule Language (SWRL) is intended to be the Semantic Web rule language. SWRL uses horn-like rules to reason among ontologies. In maintaining Semantic Web, one of the key issue is to keep rule set consistent and highly efficient. In this paper, we propose a theory to deal with the redundant rules in a SWRL rule set. The presented algorithm for detecting and eliminating implication redundancy is based on the conception of literal set closure. We also develop a refinery software tool based on the theory. The experiment results show that the proposed algorithms can deal with the redundancy in the sample rule set efficiently and effectively. Besides, the software has a module to implement reasoning tasks based on SWRL rule set.