Materializing Inferred and Uncertain Knowledge in RDF Datasets

There is a growing need for efficient and scalable semantic web queries that handle inference. There is also a growing interest in representing uncertainty in semantic web knowledge bases. In this paper, we present a bit vector schema specifically designed for RDF (Resource Description Framework) datasets. We propose a system for materializing and storing inferred knowledge using this schema. We show experimental results that demonstrate that our solution drastically improves the performance of inference queries. We also propose a solution for materializing uncertain information and probabilities using multiple bit vectors and thresholds.

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: James P. McGlothlin, Latifur R. Khan
• Keywords: AAAI 2010, Bit Vectors, Intelligent Agents, Semantic Web, Semantic Web Queries