Ontology learning methods from text - an extensive knowledge-based approach

Abstract

Abstract Ontologies are a key element of the Semantic Web. They aim to capture basic knowledge by providing appropriate terms and formal relationships between them, so that they can be used in a machine-processable manner. Accordingly they enable automatic aggregation and practical use as well as unexpected reuse of distributed data sources. Ontologies may come from many different sources, pursuing different goals and quality criteria. However, performed manually ontology construction is a very complex and tedious task, thus many methods proposed offer automatic or semi-automatic way for ontology construction. Many of the methods have their own, specific features. Therefore, this paper proposes an extensive knowledge-based approach covering the domain of ontology learning methods from text. This work aims to collect the knowledge of available approaches for ontology learning and the prominent differences between them, drawing on best practices in ontology engineering. The proposed approach refers to methods and aims to enrich knowledge in the field of ontology learning (OL). In this paper, the author’s ontology contains a set of various types of methods with main techniques used, and the necessary features in the miscellaneous approaches. The proposed an extensive knowledge-based approach uses a reasoning mechanism based on competency questions for individual approaches to determine their ontology learning method profiles. The validation stage has also been carried out. At the same time, it is an extension of the previous study in the form of a repository of knowledge about OL tools. In addition, the combination of both ontologies: tools and methods aim to provide a more efficient OL solution from text.