A Conceptual Graph-Based Method to Compute Information Content

Rolando Quintero, Miguel Torres-Ruiz (), Magdalena Saldaña-Pérez (), Carlos Guzmán Sánchez-Mejorada and Felix Mata-Rivera
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Rolando Quintero: Instituto Politécnico Nacional, Centro de Investigación en Computación, UPALM-Zacatenco, Ciudad de México 07320, Mexico
Miguel Torres-Ruiz: Instituto Politécnico Nacional, Centro de Investigación en Computación, UPALM-Zacatenco, Ciudad de México 07320, Mexico
Magdalena Saldaña-Pérez: Instituto Politécnico Nacional, Centro de Investigación en Computación, UPALM-Zacatenco, Ciudad de México 07320, Mexico
Carlos Guzmán Sánchez-Mejorada: Instituto Politécnico Nacional, Centro de Investigación en Computación, UPALM-Zacatenco, Ciudad de México 07320, Mexico
Felix Mata-Rivera: Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Ciudad de México 07340, Mexico

Mathematics, 2023, vol. 11, issue 18, 1-22

Abstract: 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.

Keywords: information content; semantic similarity; Wikipedia; conceptual distance; generality; graphs (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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