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DOI

An ontology development methodology to integrate existing ontologies in an ontology development process

01 Jan 2012-Vol. 13, Iss: 2, pp 31-61
TL;DR: This paper aims to develop a set of guidelines for the selection of ontology reuse methods which has not been explored in any ontology research, and proposes an ontology development methodology that incorporates ontology reused methods and a system that can assist to perform integration semi-automatically.
Abstract: Ontology is defined as an explicit specification of a conceptualization while a conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose. To build high quality ontologies, developers are required to choose and follow a suitable ontology development methodology in which containing a series of steps, activities and guidelines that are put together in an organized and systematic manner. Literatures show that building ontologies by reusing existing ontologies is more cost effective than building from scratch. However, majority of the methodologies only provide a very limited discussion about how to perform integration or ontology reused in their ontology development processes. This paper aims to develop a set of guidelines for the selection of ontology reuse methods which has not been explored in any ontology research. In addition, an ontology development methodology that incorporates ontology reuse methods and a system that can assist to perform integration semi-automatically is proposed. An application scenario has been developed to illustrate how the proposed development methodology can be adopted to create an ontology.

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Book ChapterDOI
01 Jan 2018
TL;DR: An automatic topic ontology construction process for better topic classification is developed and a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying concepts and their associated semantic relationships based on external knowledge from Wikipedia and WordNet is presented.
Abstract: The rapid growth of web technologies had created a huge amount of information that is available as web resources on Internet. Authors develop an automatic topic ontology construction process for better topic classification and present a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying concepts and their associated semantic relationships based on external knowledge from Wikipedia and WordNet. The topic ontology construction process relies on concept acquisition and semantic relation extraction. Initially, a topic mapping algorithm is developed to acquire the concepts from Wikipedia based on semantic relations. A semantic similarity clustering algorithm is used to compute similarity to group the set of similar concepts. The semantic relation extraction algorithm derives associated semantic relations between the set of extracted topics from the lexical patterns in WordNet. The performance of the proposed topic ontology is evaluated for the classification of web documents and obtained results depict the improved performance over ODP.

26 citations

Journal ArticleDOI
TL;DR: A corpus based novel approach to enrich the set of categories in the ODP by automatically identifying the concepts and their associated semantic relationship with corpus based external knowledge resources, such as Wikipedia and WordNet.
Abstract: Due to the explosive growth of web technology, a huge amount of information is available as web resources over the Internet. Therefore, in order to access the relevant content from the web resources effectively, considerable attention is paid on the semantic web for efficient knowledge sharing and interoperability. Topic ontology is a hierarchy of a set of topics that are interconnected using semantic relations, which is being increasingly used in the web mining techniques. Reviews of the past research reveal that semiautomatic ontology is not capable of handling high usage. This shortcoming prompted the authors to develop an automatic topic ontology construction process. However, in the past many attempts have been made by other researchers to utilize the automatic construction of ontology, which turned out to be challenging due to time, cost and maintenance. In this paper, the authors have proposed a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying the concepts and their associated semantic relationship with corpus based external knowledge resources, such as Wikipedia and WordNet. This topic ontology construction approach relies on concept acquisition and semantic relation extraction. A Jena API framework has been developed to organize the set of extracted semantic concepts, while Protege provides the platform to visualize the automatically constructed topic ontology. To evaluate the performance, web documents were classified using SVM classifier based on ODP and topic ontology. The topic ontology based classification produced better accuracy than ODP.

6 citations

Journal ArticleDOI
TL;DR: This paper is able to represent a scientifically tuned approach using the Automatic Semantic Mapping of Ontologies (ASMOV) approach and identifies a high-level Onto-Engineered framework for integrating RE and SDFO.
Abstract: A framework for integrating Requirement Engineering (RE) with scientifically tuned Digital Forensics Ontologies (SDFO) envisages a semantic web-driven approach that is able to provide a shared understanding of unifying RE techniques coupled with digital investigation techniques that are tuned from an ontological perspective. In the context of this paper, RE has been portrayed as a discipline that can not only be able to validate, specify, analyse and provide elicitation of the requirements but also to manage them effectively. Nevertheless, SDFO have been employed as bodies of knowledge that provides a shared understanding of knowledge or discipline within the Digital Forensic (DF) domain that helps to solve some given problems. Mainly, this requires the mapping/integrating of RE processes to DF tuned ontologies. The objective of the work presented in this paper, therefore, is to show how RE can be integrated into SDFO with the aim of identifying the most effective scientific approaches using an OntoTuning Matcher (OTM) that has been proposed in this paper. This paper was able to represent a scientifically tuned approach using the Automatic Semantic Mapping of Ontologies (ASMOV) approach. ASMOV provides an approach that is able to align ontologies with other systems such that inconsistencies are eliminated and the accuracy is increased. The contribution of the paper is presented in two folds: Firstly, the author identifies a high-level Onto-Engineered framework for integrating RE and SDFO, thereafter, a more detailed Onto-Engineered framework is discussed. The Onto-Engineered framework that has been discussed in this paper will help to clarify different diversification aspects that exists between RE and SDFO.

2 citations