Extracting knowledge from fuzzy relational databases with description logic
Extracting knowledge from fuzzy relational databases with description logic
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DOI：
10.3233/ica-2011-0366
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发表时间：
2011-04
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影响因子：
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通讯作者：
Zongmin Ma;Fu Zhang;Li Yan;Jingwei Cheng
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文献类型：
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作者：
Zongmin Ma;Fu Zhang;Li Yan;Jingwei Cheng
近年来，如何从模糊关系数据库中提取有用的信息和知识受到广泛关注。基于描述逻辑（DL）的高表达能力和有效的推理服务，本文提出了一种从模糊关系数据库（FRDB）自动提取知识的DL方法。为了表示提取的知识，考虑到 FRDB 的特点，引入了一种称为 f-ALCNI 的模糊深度学习。在此基础上，我们提出了一种从FRDB中提取f-ALCNI知识库的方法，即将FRDB（包括模式和数据信息）转换为f-ALCNI知识库（即TBox和ABox）。此外，我们设计并实现了一个名为 FRDB2DL 的原型提取工具。此外，为了进一步证明DL如何有助于改进某些数据库应用程序，基于提取的知识，我们通过f-ALCNI的推理机制研究了FRDB的推理问题（例如一致性、可满足性、包含、等价性和冗余）。案例研究表明，所提出的方法是可行的，并且该工具是有效的。
In recent years, how to extract useful information and knowledge from fuzzy relational databases has received much attention. Based on the high expressive power and effective reasoning service of Description Logics (DLs), this paper proposes a DL approach for automatically extracting knowledge from fuzzy relational databases (FRDB). To represent the extracted knowledge, a fuzzy DL called f-ALCNI is introduced after considering the characteristics of FRDB. On this basis, we propose an approach which can extract the f-ALCNI knowledge base from the FRDB, i.e., which can transform the FRDB (including schema and data information) into the f-ALCNI knowledge base (i.e., TBox and ABox). Furthermore, we design and implement a prototype extraction tool called FRDB2DL. In addition, to further demonstrate how the DLs are useful for improving some database applications, based on the extracted knowledge, we investigate the reasoning problems of FRDB (e.g., consistency, satisfiability, subsumption, equivalence, and redundancy) by means of the reasoning mechanism of f-ALCNI. Case studies show that the proposed approach is feasible and the tool is efficient.