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.

CONCEPT SIMILARITY IN FUZZY FORMAL CONCEPT ANALYSIS FOR SEMANTIC WEB

2010 ◽  
Vol 18 (02) ◽  
pp. 153-167 ◽  
Author(s):  
ANNA FORMICA
Keyword(s):  
Semantic Web ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Formal Concept ◽  
Fuzzy Data ◽  
Digital Resources ◽  
Fuzzy Formal Concept ◽  
Fuzzy Formal Concept Analysis ◽  
The Web ◽  
Concept Similarity

This paper presents a method for evaluating concept similarity within Fuzzy Formal Concept Analysis. In the perspective of developing the Semantic Web, such a method can be helpful when the digital resources found on the Internet cannot be treated equally and the integration of fuzzy data becomes fundamental for the search and discovery of information in the Web.

A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure

2019 ◽  
Vol 38 (2) ◽  
pp. 399-419 ◽  
Author(s):  
M. Priya ◽  
Aswani Kumar Ch.
Keyword(s):  
Social Network ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Formal Concept ◽  
Semantic Similarity Measure ◽  
Content Type ◽  
Ontology Merging ◽  
Academic Social Network

Purpose The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure. Design/methodology/approach The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part. Findings The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain. Practical implications In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers. Originality/value The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

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