Methodology for Automatic Ontology Generation Using Database Schema Information

Abstract

An ontology is a model language that supports the functions to integrate conceptually distributed domain knowledge and infer relationships among the concepts. Ontologies are developed based on the target domain knowledge. As a result, methodologies to automatically generate an ontology from metadata that characterize the domain knowledge are becoming important. However, existing methodologies to automatically generate an ontology using metadata are required to generate the domain metadata in a predetermined template, and it is difficult to manage data that are increased on the ontology itself when the domain OWL (Ontology Web Language) individuals are continuously increased. The database schema has a feature of domain knowledge and provides structural functions to efficiently process the knowledge-based data. In this paper, we propose a methodology to automatically generate ontologies and manage the OWL individual through an interaction of the database and the ontology. We describe the automatic ontology generation process with example schema and demonstrate the effectiveness of the automatically generated ontology by comparing it with existing ontologies using the ontology quality score.

Knowledge modeling: A survey of processes and techniques

“…At the same time, they also developed a prototype tool named PN2OWL to automatically transform Petri nets into ontology. An and Park 102 proposed a method to automatically generate ontologies and manage the OWL individual through the interaction between database and ontology. The classes and attributes needed for ontology construction were obtained by analyzing the table information of the database, and then the instance data in the database were mapped to the ontology model through specific rules to implement the automatic construction of ontology.…”

Ontology learning from object-relational mapping metadata and relational database

“…Hazber et al [10] proposed a two-phase method involving the construction of an ontology schema based on the RDB schema and the extraction of ontology instances from RDB data using mapping rules. An and Park [11] introduced an approach to generate an ontology model from database metadata, translating it into database tuples, particularly handling multiple individuals within the ontology.…”

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“…Document processing is tremendous due to its diversity; even document generation could be in different ways, i.e., handwritten, scanned-handwritten, typewritten, machine-generated, machine-printed. In terms of content, again it is possible to divide among purpose (business, legal, technical, medical, academic, historical) (Ritter et al, 2011) (An and Park, 2018), language (English, Hindi, Arabic, Multi-lingual) (Tao et al, 2014).…”