GARUM: A Semantic Similarity Measure Based on Machine Learning and Entity Characteristics

2018 ◽  
pp. 169-183 ◽  
Author(s):  
Ignacio Traverso-Ribón ◽  
Maria-Esther Vidal
Keyword(s):  
Machine Learning ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Semantic Similarity Measure

A Semantic Similarity Measure between Ontological Concepts

2012 ◽  
Vol 38 (2) ◽  
pp. 229-235 ◽  
Author(s):  
Wen-Qing LI ◽  
Xin SUN ◽  
Chang-You ZHANG ◽  
Ye FENG
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Semantic Similarity Measure

A Semantic Similarity Measure for the SIMS Framework

2008 ◽  
pp. 285-292
Author(s):  
Roberto Pirrone ◽  
Giuseppe Russo ◽  
Pierluca Sangiorgi ◽  
Nunzio Ingraffia ◽  
Claudia Vicari
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Semantic Similarity Measure

A Similarity Measure across Ontologies for Web Services Discovery

2016 ◽  
Vol 11 (1) ◽  
pp. 22-43 ◽  
Author(s):  
Aissa Fellah ◽  
Mimoun Malki ◽  
Atilla Elci
Keyword(s):  
Web Services ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Development Process ◽  
Semantic Web Services ◽  
Service Oriented Architectures ◽  
Semantic Similarity Measure ◽  
Generic Framework ◽  
Service Oriented ◽  
Novel Algorithm

Given the critical and difficult nature of discovering Web services in the development process of service oriented architectures, several studies have been proposed to solve this problem. There is a real need to work for matching semantic Web services which use different ontologies. In responding to this need, measuring semantic similarity between SWS may be reduced to the calculation of similarity between ontological concepts. This work is a contribution to achieve semantic interoperability for Web services in a multi-ontology environment, for which the authors present a generic framework for Web services discovery. Here their focus is on the semantic similarity measure-based core of their framework and the authors present a novel algorithm for concepts matching between different ontologies. Results of the experiments confirm the viability of the semantic similarity measure.

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