A Novel and Hybrid Ontology Ranking Framework using Semantic Closeness Measure

Abstract

Semantic Web is a Web that adds more meaning to the Web documents in order to access knowledge instead of unstructured material and also allowing knowledge to be processed automatically. One of the methods to achieve this is of using Ontology. The Ontology defines the terms and the relations among the terms on a domain. There are number of Ontology repositories present. When this increases day by day, the need for getting relevant ontology for the search keyword also increases. Even though there are number of semantic web search engines, Swoogle is placed first, which ranks the ontologies using an adaptation of Google's Page Rank scoring method. A major drawback with this system is that many ontologies are poorly inter-referenced, which does not reflect the quality of the ontologies. This paper reviews the methodologies used in Swoogle for computing rank score and proposes Semantic Closeness Measure (SCM) which has not been employed in any other ontology ranking algorithms. This work develops a hybrid ranking system to rank the ontologies better than Swoogle and other ontology search engines. The results confirm that the proposed system places the highly relevant and quality ontologies on the top list by reranking the Swoogle's results. This ranking framework enables the searcher to get relevant results quickly and reduces time in searching the long list of results.

E-learning project assessment: A new approach through the analysis of learners’ posts on social media

“…This research was carried out by means of the ontology search engine Swoogle (http://swoogle.umbc.edu), a search engine for the semantic web that crawls the web for a special class of web documents, called Semantic Web Documents. As said by Sridevi and Umarani [41], even though there are a number of semantic web search engines, Swoogle is rated as best, because it ranks the ontologies using an adaptation of the Google Page Rank scoring method.…”

Similarity algorithm for evaluating the coverage of domain ontology for semantic Web services

“…Besides, synonym tagging is also used to detect the similarity of keywords [8], [17]. Research done by [25] hybrid several ontology evaluation approach including synonym types of similarity measures that focused on biomedical domain ontology.…”

“…Our study aims on the domain of Travel. Related study has proposed to follow WordNet as frame of references but focusing on different area, for example publication domain [17] and programming domain [24].…”

“…This is to ensure that the ontology state terms related to domain knowledge it represented. As references, we refer to questions aims towards coverage criteria from [13] and enhance with related work that related towards synonym similarity string checking [17].…”