書誌事項
- タイトル別名
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- Similarity-Driven Knowledge Revision for Intensional Errors
- ルイジセイ ノ カンサツ ニ モトヅク チシキ ベース ノ ナイホウテキ エラー シュウセイホウ
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抄録
In this paper, we propose a new framework of knowledge revision, Similarity-Driven Knowledge Revision. For an object-oriented knowledge base KB, our revision is triggered when a similarity between sort concepts detected from KB does not fit a user's intuition. We revise KB into a knowledge base from which such an undesirable similarity is not detected and in which the logical semantics of KB is still preserved. An observation of undesirable similarity is due to an over-general typing of variable in KB. In order to modify the typing, we introduce a notion of extended sorts that can be viewed as a sort concept not appearing explicitly in the original knowledge base. If a variable typing with some sort is considered over-general, the typing is modified by replacing it with more specific extended sort. Such an extended sort can be efficiently identified by forward reasoning with SOL-resolution from the original knowledge base. Some experimental results show that the use of SOL-resolution can drastically improve the computational efficiency.
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 18 (1), 1-14, 2003
一般社団法人 人工知能学会
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詳細情報 詳細情報について
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- CRID
- 1390001205108709248
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- NII論文ID
- 10022004019
- 40006651554
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- NII書誌ID
- AA11579226
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- ISSN
- 13468030
- 13460714
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- NDL書誌ID
- 7264060
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可