Title: An efficient clustering approach for fair semantic web content retrieval via tri-level ontology construction model with hybrid dragonfly algorithm
Authors: R. Bhavani; V. Prakash; K. Chitra
Addresses: SCSVMV University, Sri Jayendra Saraswathi Street, Enathur, Kanchipuram, Tamil Nadu 631 561, India; SASTRA Deemed To Be University, Trichy-Tanjore Road, Thirumalaisamudram, Thanjavur, Tamil Nadu 613401, India ' SASTRA Deemed To Be University, Trichy-Tanjore Road, Thirumalaisamudram, Thanjavur, Tamil Nadu 613401, India ' Computer Science, Govt. Arts and Science College, Melur, India
Abstract: Web pages are heterogeneous and complex and there exists complicated associations within one web pages and linking to the others. The high interactions between terms in pages demonstrate vague and ambiguous meanings. Efficient and effective clustering methods are needed to discover latent and coherent meanings in context are necessary. This paper proposes an efficient clustering approach for fair semantic web content retrieval based on tri-level ontology construction model with hybrid dragonfly algorithm. Initially the query processing phase, by making use of systematic adaptive hierarchy method (SAHM) efficient ontology selection process is carried out by means of matching keywords retrieved form user query. Secondly, fuzzy sensitive near-neighbour influence (FSNI) based clustering approach relied on the ontology driven fuzzy linguistic measure, applied to estimate the uncertainty that may be relevant to the semantic content which belongs to the user quires. The proposed FSNI clustering approach with HDA algorithm performance is be evaluated and compared with existing clustering approaches in terms of retrieval accuracy and surfing time.
Keywords: systematic adaptive hierarchy method; SAHM; linear projection based self organised map; SOM; additive normalised-point wise mutual information; AN-PMI; hybrid dragonfly algorithm; HAD; tri-level ontology model construction; fuzzy sensitive near-neighbour influence (FSNI)-based clustering.
International Journal of Business Intelligence and Data Mining, 2019 Vol.14 No.1/2, pp.62 - 88
Received: 07 Apr 2017
Accepted: 08 Dec 2017
Published online: 11 Dec 2018 *