Rule-based hidden relation recognition for large scale knowledge graphs

Abstract

Abstract Knowledge graphs usually contain much implicit semantic information, which need to be further recognized through semantic inference. However, existing approaches are either not good at processing large scale data or not powerful enough for digging hidden relations thoroughly. This paper proposes a distributed OWL2 RL/RDF rule-based theory closure reasoning algorithm, named KGRL, for recognizing hidden relations in knowledge graphs. Since hidden relations derived from knowledge graph usually contain a lot of redundancies, a redundancy reduction strategy is proposed for eliminating redundant data without effect further queries on the knowledge graph. Extensive experiments and comprehensive evaluations are conducted. The experimental result shows that KGRL recognizes more hidden relations efficiently than Cichlid at different scales of the LUBM benchmark, and it only has a constant increase of runtime. Further more, the redundancy reduction strategy effectively reduces the size of the resulting knowledge graphs of hidden relation recognition on both synthetic and real-world knowledge graphs.