A Survey on Ontology Creation Methodologies

Abstract

In the current literature of knowledge management and artificial intelligence, several different approaches to the problem have been carried out of developing domain ontologies from scratch. All these approaches deal fundamentally with three problems: (1) providing a collection of general terms describing classes and relations to be employed in the description of the domain itself; (2) organizing the terms into a taxonomy of the classes by the ISA relation; and (3) expressing in an explicit way the constraints that make the ISA pairs meaningful. Though a number of such approaches can be found, no systematic analysis of them exists which can be used to understand the inspiring motivation, the applicability context, and the structure of the approaches. In this paper, we provide a framework for analyzing the existing methodologies that compares them to a set of general criteria. In particular, we obtain a classification based upon the direction of ontology construction; bottom-up are those methodologies that start with some descriptions of the domain and obtain a classification, while top-down ones start with an abstract view of the domain itself, which is given a priori. The resulting classification is useful not only for theoretical purposes but also in the practice of deployment of ontologies in Information Systems, since it provides a framework for choosing the right methodology to be applied in the specific context, depending also on the needs of the application itself.

Using a software process for ontology-based context-aware computing

“…The (u1) analyst should map the (d2) context information document to ontological terminology such as concepts, relations and axioms. In this case, it is recommended that the analyst be supported by an ontology engineer, who must follow an ontology development methodology such as the ones described in [13]. Once identified those ontological terms, the analyst should match them to the concepts, relations, and axioms of the (g1) document, what allows the identification of terms that the application can reuse from that document.…”

An ontological approach to represent knowledge in territorial planning science

“…In the last few years, a huge number of methodologies and tools to create, manage, represent, and match ontologies have been proposed and implemented in several contexts, often for ad hoc purposes (e.g., to map well-known knowledge bases or represent specific knowledge domains); a complete discussion of these is outside the scope of our work, but useful books, surveys and ad hoc models are available in the literature (e.g., Cristani & Cuel, 2005;Euzenat & Shvaiko, 2007;Denny, 2004;van Assem, Gangemi, & Schreiber, 2006;Huang & Zhou, 2007). After analyzing these resources, we argue that the main novelties of our model are that its independence from the particular domain of interest; that our linguistic approach provides a simple and general way to represent knowledge; that the strong formalization of our model is useful to integrate (directly or using ad hoc match strategies) general knowledge bases; that the implementation of our model in a software tool using a 3D interface provides an easy way to create and manipulate ontologies; and that the use of a standard language to represent our ontologies facilitates sharing and reusing knowledge.…”

An intelligent system for semi-automatic evolution of ontologies

“…An ontology is engineered by members of a domain by explicating a reality as a set of agreed terms and logically founded constraints on their use [6]. The ontology construction either uses a bottom-up methodology which starts with some descriptions of the domain and then obtains a classification or a topdown methodology which starts with an abstract view of the domain itself, which is given a priori [18]. One of the most important components of ontologies are concept hierarchies, which model the information of the domain in terms of Fig.…”