simπ: A Concept Similarity Measure under an Agent’s Preferences in Description Logic ELH

2016 ◽  
Author(s):  
Teeradaj Racharak ◽  
Boontawee Suntisrivaraporn ◽  
Satoshi Tojo
Keyword(s):  
Similarity Measure ◽  
Description Logic ◽  
Concept Similarity

Personalizing a Concept Similarity Measure in the Description Logic DLELH with Preference Profile

2018 ◽  
Vol 37 (3) ◽  
pp. 581-613 ◽  
Author(s):  
Teeradaj Racharak ◽  
Boontawee Suntisrivaraporn ◽  
Satoshi Tojo
Keyword(s):  
Similarity Measure ◽  
Description Logic ◽  
Preference Profile ◽  
Concept Similarity

Concept Similarity Measure with Hierarchy Structure and Information

2015 ◽  
Vol 8 (8) ◽  
pp. 303-314
Author(s):  
Cong Dai ◽  
Dongmei Li ◽  
Hui Han ◽  
Qichen Han ◽  
Jiajia Hou
Keyword(s):  
Similarity Measure ◽  
Concept Similarity

scholarly journals Toward semantic similarity measure between concepts in an ontology

2019 ◽  
Vol 14 (3) ◽  
pp. 1356
Author(s):  
Suwan Tongphu
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Description Logic ◽  
Classical Problem ◽  
Similarity Score ◽  
New Method ◽  
Snomed Ct ◽  
Major Drawback ◽  
Semantic Similarity Measure ◽  
Concept Definition

<p>A similarity measure is one classical problem in Description Logic which aims at identifying the similarity between concepts in an ontology. Finding a hierarchy distance among concepts in an ontology is one popular technique. However, one major drawback of such a technique is that it usually ignores a concept definition analysis. This work introduces a new method for similarity measure. The proposed system semantically analyzes structures of two concept descriptions and then computes the similarity score based on the number of shared features. The efficiency of the proposed algorithm is measured by means of the satisfaction of desirable properties and intensive experiments on the Snomed ct ontology.</p>

scholarly journals Document Plagiarism Detection Using a New Concept Similarity in Formal Concept Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jirapond Muangprathub ◽  
Siriwan Kajornkasirat ◽  
Apirat Wanichsombat
Keyword(s):  
Similarity Measure ◽  
Knowledge Construction ◽  
Formal Concept Analysis ◽  
Web Application ◽  
Information Structure ◽  
Concept Analysis ◽  
Formal Concept ◽  
Plagiarism Detection ◽  
Formal Similarity ◽  
Concept Similarity

This paper proposes an algorithm for document plagiarism detection using the provided incremental knowledge construction with formal concept analysis (FCA). The incremental knowledge construction is presented to support document matching between the source document in storage and the suspect document. Thus, a new concept similarity measure is also proposed for retrieving formal concepts in the knowledge construction. The presented concept similarity employs appearance frequencies in the obtained knowledge construction. Our approach can be applied to retrieve relevant information because the obtained structure uses FCA in concept form that is definable by a conjunction of properties. This measure is mathematically proven to be a formal similarity metric. The performance of the proposed similarity measure is demonstrated in document plagiarism detection. Moreover, this paper provides an algorithm to build the information structure for document plagiarism detection. Thai text test collections are used for performance evaluation of the implemented web application.

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