This paper gives a way to evaluate the semantic similarity between models in Knowledge Graphs (KGs) which wires Word Net and DBpedia. Past work on semantic equivalence systems have focused on either the structure of the semantic gathering among measures (e.g. Heading period and constrain), or just on the Information Content (IC) of models. We recommend a semantic likeness methodology, specifically w way, to solidify these two procedures, the usage of IC to weight the most concise way time period among musings. General corpus-basically based IC is handled from the transports of considerations over printed corpus that is required to set up a site corpus containing remarked on benchmarks and has extreme computational charge. As cases are starting at now isolated from scholarly corpus and elucidated through considerations in KGs, graph based completely IC is proposed to enroll IC essentially in light of the disseminations of thoughts over events. Through tests accomplished on extensively saw articulation resemblance datasets, we demonstrate that the wpath semantic likeness methodology has conveyed quantifiably full-gauge change over other semantic similarity procedures. Additionally, in an honest to goodness grouping make examination, the w way system has exhibited the five star executions with respect to precision and F rating.
Semantic Relatedness, Semantic Similarity, Information Content, Word Net, Knowledge Graph, DBpedia
PANCHUMARTHY GOPICHAND , B.SAI JYOTHI "A Novel Approach For Estimating Concept Semantic Similarity In Knowledge Graph" Iconic Research And Engineering Journals Volume 1 Issue 10 2018 Page 101-104
PANCHUMARTHY GOPICHAND , B.SAI JYOTHI "A Novel Approach For Estimating Concept Semantic Similarity In Knowledge Graph" Iconic Research And Engineering Journals, 1(10)