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A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database

Department of Information Systems, Faculty of Informatics, Eötvös Loránd University (ELTE), 1117 Budapest, Hungary
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Author to whom correspondence should be addressed.
Current address: Pázmány Péter Sétány 1/C, 1117 Budapest, Hungary.
Academic Editors: Giuseppe Psaila and Paolo Fosci
Information 2021, 12(5), 188; https://doi.org/10.3390/info12050188
Received: 24 March 2021 / Revised: 15 April 2021 / Accepted: 18 April 2021 / Published: 26 April 2021
(This article belongs to the Special Issue Semantic Web and Information Systems)
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. View Full-Text
Keywords: consistency checking; ontology learning; model checking; graph intermediate representation; relational database consistency checking; ontology learning; model checking; graph intermediate representation; relational database
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MDPI and ACS Style

Ma, C.; Molnár, B.; Benczúr, A. A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database. Information 2021, 12, 188. https://doi.org/10.3390/info12050188

AMA Style

Ma C, Molnár B, Benczúr A. A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database. Information. 2021; 12(5):188. https://doi.org/10.3390/info12050188

Chicago/Turabian Style

Ma, Chuangtao, Bálint Molnár, and András Benczúr. 2021. "A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database" Information 12, no. 5: 188. https://doi.org/10.3390/info12050188

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