Abstract
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.
Highlights
We presented aautomatic semantic consistency checking method based on the graph intermediate representation and model checking to check the semantic consistency while learning ontology from relational database (RDB)
We formalized the semantic correspondence between ontologies and its original data model
We converted the problem of semantic consistency checking into the global model checking problem that was eventually solved automatically by the nuXmv model checker
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ontology learning (OL) is a kind of knowledge representation learning method, aiming to (semi-)automatically construct ontologies from various data, in which the entities and relationships are usually identified and extracted based on semantic computation and knowledge inference. Due to the various naming conventions of entities and attributes, and the different semantic contexts that exist in different databases, it is unavoidable that semantic collisions and inconsistencies will occur between learned ontologies and their original databases. How to efficiently identify the inconsistencies between learned ontologies and their original databases is one of the critical tasks in the (semi-)automatic ontology learning from RDB. To address the above issues, this article presents a semi-automatic semantic consistency checking method based on the graph intermediate representation and model checking.
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