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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
Version:
Accepted Manuscript

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Department:
Oxford, MPLS, Computer Science
Role:
Author
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Department:
Oxford, MPLS, Computer Science
Role:
Author
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Department:
Oxford, MPLS, Computer Science
Role:
Author
Royal Society More from this funder
Publisher:
AAAI Press Publisher's website
Pages:
1266-1272
Publication date:
2017-02-05
Acceptance date:
2016-12-01
ISSN:
2159-5399
Pubs id:
pubs:668409
URN:
uri:133e642f-a867-46c1-a577-eca307fda4d7
UUID:
uuid:133e642f-a867-46c1-a577-eca307fda4d7
Local pid:
pubs:668409

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