Phases in Ontology Building Methodologies: A Recent Review

Phases in Ontology Building Methodologies: A Recent Review

Kamal Badr Abdalla Badr, Afaf Badr Abdalla Badr, Mohammad Nazir Ahmad
DOI: 10.4018/978-1-4666-1993-7.ch006
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Although there are several established methodologies in the ontological engineering field, many researchers tend to only loosely couple their ontology development with these methodologies, because the maturity of the methodologies has been questioned. In this chapter, the authors pay special attention to the ontology development process with a particular focus on analyzing the phases proposed by these methodologies with the objective of creating and proposing suitable phases for building ontology, covering the drawbacks of the existing methodologies for building ontology, while at the same time benefiting from the advantages included in them. Since ontology is becoming increasingly important for supporting a diversity of activities of knowledge management processes, in general, and for enterprise systems in particular, it is important to understand the existing work and how researchers develop their ontologies. Within this context, the authors analyze three ontologies that have been developed in the area of enterprise systems and knowledge management to see the conformance of each ontology development with respect to the proposed phases. As a result, they find that the three developed ontologies reflect the lack of standard and general methodological guidelines, which in turn leads to the production of a not well-informed ontology, hindrance to reuse, and extensive and time-consuming work in the development process.
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Ontology is a fundamental branch of metaphysics, which is in turn a division of philosophy, concerned with the nature and relations of being. The first time the term “ontology” appeared in the computer and information science literature was in 1967, in a work on the foundations of data modelling by Mealy (1967), and then in computer science, in the sub-field of Artificial Intelligence (AI), which made use of what is known as domain ontology. Ontology in AI was first used by Hayes (1978) in his development of the naive physics ontology of liquids and since then, a huge number of domain ontologies have been developed in numerous fields and subject areas in order to utilize the power of conceptualization that ontologies offer in developing better Knowledge Bases (KBs) and systems.

Ontologies are specifications of a conceptualization in a certain domain (Gruber, 2009). An ontology contains concepts and relations, and their definitions, properties and constraints expressed as axioms. It is not only a categorization of terms, but a fully axiomatized theory to represent knowledge of specific domains (Guarino, Oberle, & Staab, 2009; Herre, 2010). It can also support mutual understanding among developers, analysts, and stakeholders and facilitate knowledge sharing in the domain under consideration.

Nowadays, ontologies are widely used and their roles are many (Gómez-Pérez, Fernández-López, & Corcho, 2004). These roles include: ontology applications for designing and utilizing information systems, which are motivated by the necessity of integrating various information representations, deployed on different, sometimes heterogeneous platforms and for providing a collaborative environment for collectively working on common problems and tasks (De Vasconcelos, Kimble, & Rocha, 2003; Fonseca, Egenhofer, Davis, & Câmara, 2002; Guarino, 1998); and ontology applications for modelling an enterprise, which is a collection of terms and definitions that are relevant to a business enterprise. The role of enterprise modelling is to achieve model-driven enterprise design, analysis and operations (Fox & Gruninger, 1994; Grüninger, Atefi, & Fox, 2000; Uschold, King, Moralee, & Zorgios, 1998), organization of content in websites, categorization of products in e-commerce, structured and comparative searches of digital content, standard vocabularies in expert domains, product configuration in manufacturing, among many other outcomes (McCuinness, 2003).

In order for ontology to serve its intended purpose, it should be well developed and designed to reflect the piece of reality or the domain of discourse, but developing ontology is not an easy task and requires time, resources and intensive analysis (Chandrasekaran, Josephson, & Benjamins, 1999; Ding & Foo, 2002; Fonou-Dombeu & Huisman, 2011; Navigli & Velardi, 2004). Although ontology technology is maturing and there are a number of methodologies that have been used to develop the well-established ontologies, developers do not firmly associate ontology development with these methodologies because their maturity has been questioned (Corcho, Fernández-López, & Gómez-Pérez, 2003; Fernández-López & Gómez-Pérez, 2002). Methodology is considered mature when it is widely accepted, can be used and applied with satisfactory prospects of success, and can reduce the effort and time that is wasted in creating new design criteria and principles in the development process (Gomez-Perez, Fernández-López, & Corcho, 2003).

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