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A Conceptual Graph-Based Method to Compute Information Content

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rolando Quintero & Miguel Torres-Ruiz & Magdalena Saldaña-Pérez & Carlos Guzmán Sánchez-Mejorada & Felix Mata-Rivera, 2023. "A Conceptual Graph-Based Method to Compute Information Content," Mathematics, MDPI, vol. 11(18), pages 1-22, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3972-:d:1242994
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    References listed on IDEAS

    as
    1. Sandeep Kumar & Niyati Baliyan & Shriya Sukalikar, 2017. "Ontology Cohesion and Coupling Metrics," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(4), pages 1-26, October.
    2. Jorge Martinez-Gil & José F. Aldana-Montes, 2013. "Semantic similarity measurement using historical google search patterns," Information Systems Frontiers, Springer, vol. 15(3), pages 399-410, July.
    3. Silvia Likavec & Francesco Osborne & Federica Cena, 2015. "Property-based Semantic Similarity and Relatedness for Improving Recommendation Accuracy and Diversity," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 11(4), pages 1-40, October.
    4. Zenun Kastrati & Ali Shariq Imran & Sule Yildirim-Yayilgan, 2016. "SEMCON: A Semantic and Contextual Objective Metric for Enriching Domain Ontology Concepts," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 12(2), pages 1-24, April.
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    Cited by:

    1. Rolando Quintero & Esteban Mendiola & Giovanni Guzmán & Miguel Torres-Ruiz & Carlos Guzmán Sánchez-Mejorada, 2023. "Algorithm for the Accelerated Calculation of Conceptual Distances in Large Knowledge Graphs," Mathematics, MDPI, vol. 11(23), pages 1-30, November.

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