An efficient clustering approach for fair semantic web content retrieval via tri-level ontology construction model with hybrid dragonfly algorithm. (2019)

• Record Type: Journal Article
• Authors: Bhavani, R.; Prakash, V.; Chitra, K.
• Is Part Of: International journal of business intelligence and data mining. Volume 14:Number 1/2(2019)
• Page Start: 62
• Page End: 88
• Publication Date: 2019
• Subjects: 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; 006.312
• Languages: English
• ISSNs: 1743-8187

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.