Knowledge systematization for ontology learning methods

The need for interoperable semantics in modern information systems forces to develop more and more intelligent solutions. The increasing demand for these solutions, the explosion of various types of information and the technological development pose new challenges and requirements. Ontologies are often viewed as the answer to this need. The connections between ontologies and Semantic Web become a very promising area. The Semantic Web's success is dependent on the quality of its underline ontologies, whereas ontologies provide a shared and a common understanding of a domain enabling communication between people and heterogeneous and distributed systems. However, key issue helps ontologies to power the Semantic Web have made ontology learning from various data sources a very auspicious field of research. It aims at semi-automatically or automatically building ontologies from given data sources with a limited human exert. A huge number of available approaches for ontology learning and the prominent differences between them cause the necessity of knowledge systematization for this domain. The paper yields the author's proposal of ontological elaboration for methods for ontology learning and their features, providing formal, practical and technological guidance to knowledge management based approach to methods supporting ontology learning.