The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Information Retrieval: Algorithms and Heuristics (The Kluwer International Series on Information Retrieval)
Soft Computing in Ontologies and Semantic Web (Studies in Fuzziness and Soft Computing)
Soft Computing in Ontologies and Semantic Web (Studies in Fuzziness and Soft Computing)
Ontology-Based Fuzzy Semantic Clustering
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 02
Semantic Web Programming
A fuzzy ontological knowledge document clustering methodology
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic annotation, indexing, and retrieval
Web Semantics: Science, Services and Agents on the World Wide Web
Ranking web sites using domain ontology concepts
Information and Management
Expert Systems with Applications: An International Journal
A Web Search Engine-Based Approach to Measure Semantic Similarity between Words
IEEE Transactions on Knowledge and Data Engineering
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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With the massive growth and large volume of the web it is very difficult to recover results based on the user preferences. The next generation web architecture, semantic web reduces the burden of the user by performing search based on semantics instead of keywords. Even in the context of semantic technologies optimization problem occurs but rarely considered. In this paper Document clustering is applied to recover relevant documents. We propose a ontology based clustering algorithm using semantic similarity measure and Particle Swarm Optimization(PSO), which is applied to the annotated documents for optimizing the result. The proposed method uses Jena API and GATE tool API and the documents can be recovered based on their annotation features and relations. A preliminary experiment comparing the proposed method with K-Means shows that the proposed method is feasible and performs better than K-Means.