文章基本信息

标题：A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database
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作者：Chuangtao Ma ; Bálint Molnár ; András Benczúr 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：5
页码：188
DOI：10.3390/info12050188
出版社：MDPI Publishing
摘要：To tackle the issues of semantic collision and inconsistencies between ontologies and the original data model while learning ontology from relational database (RDB), a semi-automatic semantic consistency checking method based on graph intermediate representation and model checking is presented. Initially, the W-Graph, as an intermediate model between databases and ontologies, was utilized to formalize the semantic correspondences between databases and ontologies, which were then transformed into the Kripke structure and eventually encoded with the SMV program. Meanwhile, description logics (DLs) were employed to formalize the semantic specifications of the learned ontologies, since the OWL DL showed good semantic compatibility and the DLs presented an excellent expressivity. Thereafter, the specifications were converted into a computer tree logic (CTL) formula to improve machine readability. Furthermore, the task of checking semantic consistency could be converted into a global model checking problem that could be solved automatically by the symbolic model checker. Moreover, an example is given to demonstrate the specific process of formalizing and checking the semantic consistency between learned ontologies and RDB, and a verification experiment was conducted to verify the feasibility of the presented method. The results showed that the presented method could correctly check and identify the different kinds of inconsistencies between learned ontologies and its original data model.
关键词：consistency checking; ontology learning; model checking; graph intermediate representation; relational database consistency checking ; ontology learning ; model checking ; graph intermediate representation ; relational database