Abstract
It is now widely accepted that ontologies play a critical role in achieving the goal of machine understandable web, also known as semantic web. In order to develop ontologies, several methodologies have been proposed during the last two decades. Despite the fact, that quite a number of ontology engineering methodologies have been proposed, still the field lacks widely accepted and mature methodologies. Most methodologies lack sufficient details of techniques and activities employed in them. However, some methodologies provide sufficient details including METHONTOLOGY. This article discusses and reports a critical analysis and comparison of these methodologies. The analysis is performed based on a criterion, derived from related literature, trends and needs which evolved over the years. The results of the analysis showed that there is no completely mature methodology. Therefore, this research may act as a preliminary guide to come with a state of art ontology engineering methodology, bridging up the existing gaps and shortfalls.
Highlights
The future of machine understandable web, the semantic web has its root deep down in to ontologies
In the present day ontologies are extensively used in different domains like knowledge engineering, artificial intelligence, natural language processing, ecommerce, intelligent information integration, information retrieval, database design and integration, bio-informatics and etc
In order to support the development of ontologies several methodologies have been proposed to date, facilitating the process of ontology development or ontology engineering
Summary
The future of machine understandable web, the semantic web has its root deep down in to ontologies. Ontologies explicitly define the concepts in a domain and the relationships between those concepts. They are critical for many knowledge based applications since they are the formal model and detailed machine understandable description of a domain, which can be utilized in many ways. There are several definitions available to define ontology in different fields. In the present day ontologies are extensively used in different domains like knowledge engineering, artificial intelligence, natural language processing, ecommerce, intelligent information integration, information retrieval, database design and integration, bio-informatics and etc. In order to support the development of ontologies several methodologies have been proposed to date, facilitating the process of ontology development or ontology engineering
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