Materializing Inferred and Uncertain Knowledge in RDF Datasets

AAAI 2010 pp. 1951-1952

doi:10.1609/AAAI.V24I1.7786 /aaai/2010/mcglothlin2010aaai-materializing-a/

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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Cite

Text

McGlothlin and Khan. "Materializing Inferred and Uncertain Knowledge in RDF Datasets." AAAI Conference on Artificial Intelligence, 2010. doi:10.1609/AAAI.V24I1.7786

Markdown

[McGlothlin and Khan. "Materializing Inferred and Uncertain Knowledge in RDF Datasets." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/mcglothlin2010aaai-materializing-a/) doi:10.1609/AAAI.V24I1.7786

BibTeX

@inproceedings{mcglothlin2010aaai-materializing-a,
  title     = {{Materializing Inferred and Uncertain Knowledge in RDF Datasets}},
  author    = {McGlothlin, James P. and Khan, Latifur R.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2010},
  pages     = {1951-1952},
  doi       = {10.1609/AAAI.V24I1.7786},
  url       = {https://mlanthology.org/aaai/2010/mcglothlin2010aaai-materializing-a/}
}