Computing Semantic Similarity between ECG-Information Concepts Based on an Entropy-Weighted Concept Lattice


Kai Wang, Shu Yang, Journal of Information Processing Systems Vol. 16, No. 1, pp. 184-200, Feb. 2020

10.3745/JIPS.04.0157
Keywords: Concept Lattice Theory, ECG Concept, Entropy, Inclusion-Degree, Semantic Computing
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Abstract

Similarity searching is a basic issue in information processing because of the large size of formal contexts and their complicated derivation operators. Recently, some researchers have focused on knowledge reduction methods by using granular computing. In this process, suitable information granules are vital to characterizing the quantities of attributes and objects. To address this problem, a novel approach to obtain an entropy-weighted concept lattice with inclusion degree and similarity distance (ECLisd) has been proposed. The approach aims to compute the combined weights by merging the inclusion degree and entropy degree between two concepts. In addition, another method is utilized to measure the hierarchical distance by considering the different degrees of importance of each attribute. Finally, the rationality of the ECLisd is validated via a comparative analysis.


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Cite this article
[APA Style]
Kai Wang and Shu Yang (2020). Computing Semantic Similarity between ECG-Information Concepts Based on an Entropy-Weighted Concept Lattice. Journal of Information Processing Systems, 16(1), 184-200. DOI: 10.3745/JIPS.04.0157.

[IEEE Style]
K. Wang and S. Yang, "Computing Semantic Similarity between ECG-Information Concepts Based on an Entropy-Weighted Concept Lattice," Journal of Information Processing Systems, vol. 16, no. 1, pp. 184-200, 2020. DOI: 10.3745/JIPS.04.0157.

[ACM Style]
Kai Wang and Shu Yang. 2020. Computing Semantic Similarity between ECG-Information Concepts Based on an Entropy-Weighted Concept Lattice. Journal of Information Processing Systems, 16, 1, (2020), 184-200. DOI: 10.3745/JIPS.04.0157.