Enhanced entity-relationship modeling with description logic

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• 作者：Fu Zhang ; ^{zhangfu216@126.com" class="auth_mail" title="E-mail the corresponding author} ; Z.M. Ma ; Jingwei Cheng
• 关键词：EER (Enhanced Entity-Relationship) model ; Description Logic ; Representation ; Reasoning
• 刊名：Knowledge-Based Systems
• 出版年：2016
• 出版时间：1 February 2016
• 年：2016
• 卷：93
• 期：Complete
• 页码：12-32
• 全文大小：5492 K

文摘

Based on the high expressive powers and effective reasoning services of description logics (DLs, for short), DLs have been employed in data modeling to support the development and maintenance of data models. The basic idea is that once the correspondences between data models and DLs can be established, reasoning techniques from DLs become applicable to the reasoning of data models.

This paper proposes a complete DL approach for representing and reasoning on EER (Enhanced Entity-Relationship) models. We develop an equivalence-preserving transformation approach and a prototype tool for transforming an EER model into a DL knowledge base, and propose methods to reduce reasoning on the EER model to reasoning on the transformed DL knowledge base. As one result, the reasoning capabilities of the DL can provide the basic reasoning services that are needed in EER modeling. In detail, we firstly propose a formal definition and semantic interpretation method of EER models, which summarizes and includes all features of EER models. Then  , by analyzing the features of EER models, a DL called ALCQI_K$\mathcal{A}\mathcal{L}\mathcal{C}\mathcal{Q}{\mathcal{I}}_{\mathcal{K}}$ is presented as the language of representing and reasoning on EER models. On this basis  , we propose an approach for transforming EER models into ALCQI_K$\mathcal{A}\mathcal{L}\mathcal{C}\mathcal{Q}{\mathcal{I}}_{\mathcal{K}}$ knowledge bases. The correctness of the transformation is proved and a transformation example is provided. Further, a prototype transformation tool is implemented. Case studies show that our approach and prototype tool actually work. Finally  , based on the transformed ALCQI_K$\mathcal{A}\mathcal{L}\mathcal{C}\mathcal{Q}{\mathcal{I}}_{\mathcal{K}}$ knowledge bases, we propose methods to reduce reasoning on EER models to reasoning on the transformed ALCQI_K$\mathcal{A}\mathcal{L}\mathcal{C}\mathcal{Q}{\mathcal{I}}_{\mathcal{K}}$ knowledge bases.