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Trust-sensitive evolution of DL-lite knowledge bases

Abstract:

Evolution of Knowledge Bases (KBs) consists of incorporating new information in an existing KB. Previous studies assume that the new information should be fully trusted and thus completely incorporated in the old knowledge. We suggest a setting where the new knowledge can be partially trusted and develop model-based approaches (MBAs) to KB evolution that rely on this assumption. Under MBAs the result of evolution is a set of interpretations and thus two core problems for MBAs are closure, i.e...

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Publication status:
Published
Peer review status:
Peer reviewed

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  • (Accepted manuscript, pdf, 884.0KB)

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Royal Society More from this funder
Publisher:
AAAI Press Publisher's website
Journal:
31st AAAI Conference on Artificial Intelligence Journal website
Pages:
1266-1272
Host title:
Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17)
Publication date:
2017-02-01
Acceptance date:
2016-12-01
ISSN:
2159-5399
Source identifiers:
668409
Pubs id:
pubs:668409
UUID:
uuid:133e642f-a867-46c1-a577-eca307fda4d7
Local pid:
pubs:668409
Deposit date:
2017-01-06

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