Semantic Clustering of Web Documents: An Ontology based Approach Using Swarm Intelligence

Semantic Clustering of Web Documents: An Ontology based Approach Using Swarm Intelligence

J. Avanija (Velammal College of Engineering & Technology, Madurai, Tamilnadu, India) and K. Ramar (Einstein College of Engineering, Tirunelveli, Tamilnadu, India)
DOI: 10.4018/jitwe.2012100102
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

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. The authors propose an 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.
Article Preview

Ranking of documents is combined along with clustering by ordering the web pages in the form of clusters based on the query given by the user (Duhan & Sharma, 2010). The performance of the ordered result is measured based on relevancy. Since the classical clustering methods are not dealing with the semantics of the objects a new methodology was derived to incorporate knowledge in to clustering process (Batet, Valls, & Gibert, 2008). Fuzzy clustering scheme is combined with semantic analysis mechanism along with relevant attributes in the ontology (Thangamani & Thangaraj, 2010). Hierarchical clustering along with fuzzy logic approach is used to cluster knowledge documents along using ontology (Trappey, Trappey, Hsu, & Hsiao, 2009). Use of clustering methods provide appropriate document retrieval (Gallova, 2007).

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 13: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 12: 4 Issues (2017)
Volume 11: 4 Issues (2016)
Volume 10: 4 Issues (2015)
Volume 9: 4 Issues (2014)
Volume 8: 4 Issues (2013)
Volume 7: 4 Issues (2012)
Volume 6: 4 Issues (2011)
Volume 5: 4 Issues (2010)
Volume 4: 4 Issues (2009)
Volume 3: 4 Issues (2008)
Volume 2: 4 Issues (2007)
Volume 1: 4 Issues (2006)
View Complete Journal Contents Listing