4.7 Article

Cross domain-based ontology construction via Jaccard Semantic Similarity with hybrid optimization model

期刊

EXPERT SYSTEMS WITH APPLICATIONS
卷 178, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2021.115046

关键词

Ontology; Semantic web; Data filtering; Data annotation; Cross-domain network; Optimization

资金

  1. ASPIRE Research Grant, BCUD SPPU, Pune [-18TEC000075]

向作者/读者索取更多资源

This paper explores ontology construction model under cross-domain application, focusing on data filtering and data annotation decision-making processes. The process involves Jaccard Similarity Evaluation, data filtering, outlier detection, semantic annotation, and clustering to form ontology clusters.
Semantic web technology seems to be in the infant stage as only little efforts have been taken on ontology construction with cross-domain application. This paper intends to take an effort on a new workspace, in which the ontology construction model under cross-domain application is performed. The core concern of this work is on two decision-making process namely data filtering and data annotation. Certain process is followed in this work: (i) Preprocessing (ii) Proposed Jaccard Similarity Evaluation (iii) Data filtering and Outlier Detection (iv) Semantic annotation and clustering. More particularly, data filtering is performed based on the evaluated sim-ilarity function. The outliers are identified and grouped separately. The data annotation is performed based on the semantics and thereby the clustering process takes place to form the ontology precisely. This clustering process obviously relies to the optimization crisis as the optimal centroid selection becomes the greatest issue. In order to solve this, this paper extends with the introduction of a hybrid algorithm named Circling Insisted-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale Optimization Algorithm (WOA) and Rider Optimization Algorithm (ROA), respectively. Finally, the performance of proposed work is compared and proved over other state-of-the-art models.

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