ISSN 2996-8223
International Journal of Computer Science and Technology | Vol. 11, No. 6, June 2009 | pp. 41–48
DOI: 10.46882/2009/IJCST/000015
Article Type: Review Article
Title: A Comprehensive Review of Semantic Web Ontologies and Knowledge Representation Techniques
Names of Authors: Amelie Dubois¹, Hans-Dieter Schmidt²
Authors’ Affiliations: ¹Laboratoire d’Informatique, Universite Paris-Saclay, Paris, France; ²Institute for Applied Computer Science, Technical University of Munich, Munich, Germany
Abstract: The Semantic Web paradigm transforms unstructured web data into machine-readable knowledge frameworks to allow intelligent agent interoperability. This review paper provides a structured evaluation of state-of-the-art knowledge representation languages and ontologies developed over the past decade, including RDF, RDFS, and OWL2 profiles. We analyze their underlying description logics, expressiveness boundaries, and computational complexities during reasoning tasks. The paper details the practical limitations of current reasoners like Pellet and HermiT when executing large-scale web graphs containing over 100,000 assertions. Key architectural challenges regarding ontology alignment, semantic mapping, and schema reconciliation within decentralized federated databases are thoroughly categorized. Finally, we highlight emerging trends, such as the convergence of deep learning models with structural symbolic knowledge bases to improve natural language understanding. This synthesis provides developers and researchers with a clear roadmap for selecting optimal ontological frameworks based on scale and reasoning constraints.
Keywords: Semantic Web, Resource Description Framework, Web Ontology Language, Knowledge Representation, Ontology Alignment, Description Logics
Manuscript Timeline: Received: September 15, 2008; Revised: November 30, 2008; Accepted: January 5, 2009; Published: February 25, 2009
Citation: Dubois, A., & Schmidt, H. D. (2009). A Comprehensive Review of Semantic Web Ontologies and Knowledge Representation Techniques. International Journal of Computer Science and Technology, 11(6), 41–48.
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