A Conceptual Graph-Based Method to Compute Information Content

Quintero, Rolando; Torres-Ruiz, Miguel; Saldaña-Pérez, Magdalena; Guzmán Sanchéz-Mejorada, Carlos; Mata-Rivera, Felix

doi:10.20944/preprints202308.0093.v1

2023

DOI: 10.20944/preprints202308.0093.v1

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A Conceptual Graph-Based Method to Compute Information Content

Rolando Quintero,

Miguel Torres-Ruiz,

Magdalena Saldaña-Pérez

et al.

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 information a concept provides to others compared to the information 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 were compared to current methods … Show more

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Algorithm for the Accelerated Calculation of Conceptual Distances in Large Knowledge Graphs

Quintero,

Mendiola,

Guzmán

et al. 2023

Mathematics

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Conceptual distance refers to the degree of proximity between two concepts within a conceptualization. It is closely related to semantic similarity and relationships, but its measurement strongly depends on the context of the given concepts. DIS-C represents an advancement in the computation of semantic similarity/relationships that is independent of the type of knowledge structure and semantic relations when generating a graph from a knowledge base (ontologies, semantic networks, and hierarchies, among others). This approach determines the semantic similarity between two indirectly connected concepts in an ontology by propagating local distances by applying an algorithm based on the All Pairs Shortest Path (APSP) problem. This process is implemented for each pair of concepts to establish the most effective and efficient paths to connect these concepts. The algorithm identifies the shortest path between concepts, which allows for an inference of the most relevant relationships between them. However, one of the critical issues with this process is computational complexity, combined with the design of APSP algorithms, such as Dijkstra, which is 𝒪n3. This paper studies different alternatives to improve the DIS-C approach by adapting approximation algorithms, focusing on Dijkstra, pruned Dijkstra, and sketch-based methods, to compute the conceptual distance according to the need to scale DIS-C to analyze very large graphs; therefore, reducing the related computational complexity is critical. Tests were performed using different datasets to calculate the conceptual distance when using the original version of DIS-C and when using the influence area of nodes. In situations where time optimization is necessary for generating results, using the original DIS-C model is not the optimal method. Therefore, we propose a simplified version of DIS-C to calculate conceptual distances based on centrality estimation. The obtained results for the simple version of DIS-C indicated that the processing time decreased 2.381 times when compared to the original DIS-C version. Additionally, for both versions of DIS-C (normal and simple), the APSP algorithm decreased the computational cost when using a two-hop coverage-based approach.

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Algorithm for the Accelerated Calculation of Conceptual Distances in Large Knowledge Graphs

Quintero,

Mendiola,

Guzmán

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

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A Conceptual Graph-Based Method to Compute Information Content

Cited by 1 publication

References 88 publications

Algorithm for the Accelerated Calculation of Conceptual Distances in Large Knowledge Graphs

Algorithm for the Accelerated Calculation of Conceptual Distances in Large Knowledge Graphs

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