Materializing Inferred and Uncertain Knowledge in RDF Datasets

Authors

  • James McGlothlin The University of Texas at Dallas
  • Latifur Khan The University of Texas at Dallas

DOI:

https://doi.org/10.1609/aaai.v24i1.7786

Keywords:

semantic web, ontology, uncertainty reasoning, inference, databases, query optimization, resource description framework

Abstract

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.

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Published

2010-07-05

How to Cite

McGlothlin, J., & Khan, L. (2010). Materializing Inferred and Uncertain Knowledge in RDF Datasets. Proceedings of the AAAI Conference on Artificial Intelligence, 24(1), 1951-1952. https://doi.org/10.1609/aaai.v24i1.7786