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Knowledge structuring to support facet‐based ontology visualization
Author(s) -
Maio Carmen De,
Fenza Giuseppe,
Loia Vincenzo,
Senatore Sabrina
Publication year - 2010
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20451
Subject(s) - computer science , ontology , upper ontology , process ontology , structuring , ontology based data integration , formal ontology , domain knowledge , suggested upper merged ontology , conceptualization , semantic web , ontology alignment , information retrieval , domain (mathematical analysis) , knowledge management , artificial intelligence , economics , mathematical analysis , mathematics , philosophy , epistemology , finance
The huge growth of data on the Web and the requirement of semantic content analysis make the knowledge management and data mining very difficult activities. The knowledge elicitation, codification, and storage need not trivial techniques to improve formal information structuring on the Internet. Ontologies provide conceptualization and processing knowledge, sharing of consolidate understanding, reusing of domain knowledge codification for many Web applications. Manual construction of a domain‐specific ontology is an intensive and time‐consuming process, which requires an accurate domain expertise, because of structural and logical difficulties in the definition of concepts, as well as conceivable relationships. At the same time, the ontology visualization process requires similar endeavors to support ontology management, exploration, and browsing. This work describes an automatic method for ontology design from the content analysis of Web resources. The approach exploits a fuzzy extension of formal concept analysis model for structuring the elicited knowledge, viz. concepts and relations embedded in the resources content. Final result is an effective ontology visualization through a navigable, facet‐based view of the built ontology across the extracted concepts and their own population. Furthermore, the approach proposes a simple labeling of ontology concepts through a sketched and intuitive process. © 2010 Wiley Periodicals, Inc.