A Hybrid Concept Similarity Measure Model for Ontology Environment

2009 ◽  
pp. 848-857 ◽  
Author(s):  
Hai Dong ◽  
Farookh Khadeer Hussain ◽  
Elizabeth Chang
Keyword(s):  
Similarity Measure ◽  
Hybrid Concept ◽  
Measure Model ◽  
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 A Research on Similarity Measure to Identify Effective Similar Users in Recommender Systems

2019 ◽  
Vol 8 (9S2) ◽  
pp. 834-840
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
The Internet ◽  
Internet Users ◽  
Drastic Increase ◽  
Measure Model ◽  
Filtering Technique ◽  
Selection Of

In recent years there is a drastic increase in information over the internet. Users get confused to find out best product on the internet of one’s interest. Here the recommender system helps to filter the information and gives relevant recommendations to users so that the user community can find the item(s) of their interest from huge collection of available data. But filtering information from the users reviews given for various items seems to be a challenging task for recommending the user interested things. In general similarities between the users are considered for recommendations in collaborative filtering techniques. This paper describes a new collaborative filtering technique called Adaptive Similarity Measure Model [ASMM] to identify similarity between users for the selection of unseen items. Out of all the available items most similarities would be sorted out by ASMM for recommendation which varies from user to user

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.

A Knowledge Management Method Based on Ontology for AP1000 Nuclear

2010 ◽  
Vol 431-432 ◽  
pp. 377-380
Author(s):  
Lin Lin ◽  
Yong Jian Zhang ◽  
Lu Yin
Keyword(s):  
Knowledge Management ◽  
Particle Swarm Optimization ◽  
Similarity Measure ◽  
Nuclear Power ◽  
Knowledge Retrieval ◽  
Special Relation ◽  
Swarm Optimization ◽  
Ontology Model ◽  
Measure Model ◽  
Management Method

A knowledge management method for AP1000 nuclear is proposed. Based on the modularization characteristic of AP1000, the set of AP1000 ontology is created. Because the first nuclear power based on AP1000 is still being developed, the ontology model of AP1000 is hard to construct now. The particle swarm optimization is adopted to extract the relatively independent instance models from the complexity AP1000 instance model, which map into the conception models called basis ontology. In order to implement the comparison between two conceptions, using the idea of the DNA inheritance in a family, a similarity measure model of the conception is developed. By means of the general-special relation between the conceptions, the similarity measure model and the basis ontology, the knowledge retrieval for AP1000 is implemented.

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