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Paper Details

Paper Title
This paper gives a way for measuring the semantic similarity between standards in Knowledge Graphs (KGs) which includes WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic community among principles (e.G. Direction period and intensity), or only on the Information Content (IC) of principles. We recommend a semantic similarity technique, namely wpath, to combine these two approaches, the use of IC to weight the shortest path period among ideas. Conventional corpus-primarily based IC is computed from the distributions of ideas over textual corpus, that is required to prepare a site corpus containing annotated standards and has excessive computational fee. As instances are already extracted from textual corpus and annotated through ideas in KGs, graph-based totally IC is proposed to compute IC primarily based on the distributions of concepts over instances. Through experiments accomplished on widely recognized phrase similarity datasets, we display that the wpath semantic similarity method has produced statistically full-size improvement over other semantic similarity strategies. Moreover, in a real category type assessment, the wpath method has shown the first-class performance in terms of accuracy and F rating.
Semantic Similarity, Semantic Relatedness, Information Content, Knowledge Graph, WordNet, DBpedia
Others Details
Paper Id : 83812
Author Name : GUDA SRIDHAR
Volume/Issue No : Volume 05 Issue 01
Page No : 29-32
DOI Number : DOI:10.21090/IJAERD.83812
Publication Date : 2018-01-05
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website :
Impact Factor : 4.72, SJIF-2016
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406