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Citation: Bill MacCartney and Sheila A. McIlraith and Eyal Amir and Tomás E. Uribe. (2003) Practical Partition-Based Theorem Proving for Large Knowledge Bases. In Proceedings of the Nineteenth International Conference on Artificial Intelligence (IJCAI-03), August,2003.
| Publication inproceedings ( Edit )
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| type | InProceedings
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| bibtype | inproceedings
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| Bibtex basics
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| author | Bill MacCartney and Sheila A. McIlraith and Eyal Amir and Tomás E. Uribe
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| title | Practical Partition-Based Theorem Proving for Large Knowledge Bases
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| booktitle | Proceedings of the Nineteenth International Conference on Artificial Intelligence (IJCAI-03)
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| year | 2003
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| month | August
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| abstract | Query answering over commonsense knowledge bases typically employs a first-order logic theorem prover. While first-order inference is intractable in general, provers can often be hand-tuned to answer queries with reasonable performance in practice. Appealing to previous theoretical work on partition-based reasoning, we propose resolution-based theorem proving strategies that exploit the structure of a KB to improve the efficiency of reasoning. We analyze and experimentally evaluate these strategies with a testbed based on the SNARK theorem prover. Exploiting graph-based partitioning algorithms, we show how to compute a partition-derived ordering for ordered resolution, with good experimental results, offering an automatic alternative to hand-crafted orderings. We further propose a new resolution strategy, MFS resolution, that combines partition-based message passing with focused sublanguage resolution. Our experiments show a significant reduction in the number of resolution steps when this strategy is used. Finally, we augment partition-based message passing, partition-derived ordering, and MFS by combining them with the set-of-support restriction. While these combinations are incomplete, they often produce dramatic improvements in practice. This work presents promising practical techniques for query answering with large and potentially distributed commonsense KBs.
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| KSL Technical Report ID: KSL-03-12
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Facts about Practical Partition-Based Theorem Proving for Large Knowledge BasesRDF feed
| Abstract | Query answering over commonsense knowledge … Query answering over commonsense knowledge bases typically employs a first-order logic theorem prover. While first-order inference is intractable in general, provers can often be hand-tuned to answer queries with reasonable performance in practice. Appealing to previous theoretical work on partition-based reasoning, we propose resolution-based theorem proving strategies that exploit the structure of a KB to improve the efficiency of reasoning. We analyze and experimentally evaluate these strategies with a testbed based on the SNARK theorem prover. Exploiting graph-based partitioning algorithms, we show how to compute a partition-derived ordering for ordered resolution, with good experimental results, offering an automatic alternative to hand-crafted orderings. We further propose a new resolution strategy, MFS resolution, that combines partition-based message passing with focused sublanguage resolution. Our experiments show a significant reduction in the number of resolution steps when this strategy is used. Finally, we augment partition-based message passing, partition-derived ordering, and MFS by combining them with the set-of-support restriction. While these combinations are incomplete, they often produce dramatic improvements in practice. This work presents promising practical techniques for query answering with large and potentially distributed commonsense KBs. d potentially distributed commonsense KBs. |
| Author | Bill MacCartney and Sheila A. McIlraith and Eyal Amir and Tomás E. Uribe + |
| Bibtype | inproceedings + |
| Booktitle | Proceedings of the Nineteenth International Conference on Artificial Intelligence (IJCAI-03) + |
| Has author | Bill MacCartney and Sheila A. McIlraith and Eyal Amir and Tomás E. Uribe + |
| Has identifier | KSL-03-12 + |
| Has publishing details | August,2003 + |
| Has title | Practical Partition-Based Theorem Proving for Large Knowledge Bases + |
| Has where published | Proceedings of the Nineteenth International Conference on Artificial Intelligence (IJCAI-03) + |
| Has year | 2003 + |
| Ksl tr id | KSL-03-12 + |
| Month | August + |
| Process note | NO + |
| Title | Practical Partition-Based Theorem Proving for Large Knowledge Bases + |
| Year | 2003 + |
