A New Approach to Knowledge Base Revision in DL-Lite
A New Approach to Knowledge Base Revision in DL-Lite
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DOI：
10.1609/aaai.v24i1.7593
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
Zhe Wang;Kewen Wang;R. Topor
中科院分区：
文献类型：
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作者：
Zhe Wang;Kewen Wang;R. Topor
对于本体管理和 DL 社区来说，以与语法无关的方式修改描述逻辑 (DL) 中的知识库 (KB) 是一个重要且重要的问题。人们已经做出了一些尝试来将经典的基于模型的信念修正和更新技术应用于深度学习，但它们在几个方面受到限制。特别是，它们不提供用于通用 DL KB 修订的运算符或算法。关键的困难在于，与命题逻辑不同，深度学习知识库可能具有无限多个具有复杂（可能是无限）结构的模型，这使得根据模型定义和计算修订变得困难。在本文中，我们研究 DL-Lite 系列中特定 DL 中的通用知识库。我们引入了此类知识库的特征概念，使用特征（而不是模型）开发知识库的替代语义表征，为知识库定义了两个特定的修订运算符，并提出了第一个用于计算与语法无关的知识库修订版的最佳近似值的算法。
Revising knowledge bases (KBs) in description logics (DLs) in a syntax-independent manner is an important, nontrivial problem for the ontology management and DL communities. Several attempts have been made to adapt classical model-based belief revision and update techniques to DLs, but they are restricted in several ways. In particular, they do not provide operators or algorithms for general DL KB revision. The key difficulty is that, unlike propositional logic, a DL KB may have infinitely many models with complex (and possibly infinite) structures, making it difficult to define and compute revisions in terms of models. In this paper, we study general KBs in a specific DL in the DL-Lite family. We introduce the concept of features for such KBs, develop an alternative semantic characterization of KBs using features (instead of models), define two specific revision operators for KBs, and present the first algorithm for computing best approximations for syntax-independent revisions of KBs.