A Conceptual Graph-Based Method to Compute Information Content

被引：2
|
作者：
Quintero, Rolando [1 ]
Torres-Ruiz, Miguel [1 ]
Saldana-Perez, Magdalena [1 ]
Guzman Sanchez-Mejorada, Carlos [1 ]
Mata-Rivera, Felix [2 ]
机构：
[1] Inst Politecn Nacl, Ctr Invest Comp, UPALM Zacatenco, Mexico City 07320, Mexico
[2] Inst Politecn Nacl, Unidad Profes Interdisciplinaria Ingn & Tecnol Ava, Mexico City 07340, Mexico
来源：
MATHEMATICS | 2023年 / 11卷 / 18期
关键词：
information content; semantic similarity; Wikipedia; conceptual distance; generality; graphs; SEMANTIC SIMILARITY ESTIMATION; ENTITY CLASSES; ONTOLOGY; RELATEDNESS; WIKIPEDIA; REPRESENTATIONS; DATABASE; WORDNET; DOMAIN; TEXT;
D O I：
10.3390/math11183972
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This research uses the computing of conceptual distance to measure information content in Wikipedia categories. The proposed metric, generality, relates information content to conceptual distance by determining the ratio of the information that a concept provides to others compared to the information that it receives. The DIS-C algorithm calculates generality values for each concept, considering each relationship's conceptual distance and distance weight. The findings of this study are compared to current methods in the field and found to be comparable to results obtained using the WordNet corpus. This method offers a new approach to measuring information content applied to any relationship or topology in conceptualization.
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页数：22
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