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Analysis of institutional authors

Suarez-Figueroa, Maria Del CarmenAuthor

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December 3, 2025
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Bridging Text and Knowledge: Explainable AI for Knowledge Graph Classification and Concept Map-Based Semantic Domain Discovery with OBOE Framework

Publicated to: Applied Sciences-Basel. 15 (22): 12231- - 2025-11-18 15(22), DOI: 10.3390/app152212231

Authors:

Escobar, RAD; Suárez-Figueroa, MD; Lopez, MF; Terrazas, BV
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Affiliations

CEU San Pablo Univ, Dept Informat Technol, Madrid 28668, Spain - Author
EY Europe West Technol, EY AI Ctr Excellence, Madrid 28003, Spain - Author
Spanish Minist Educ Vocat Training & Sports, Madrid 28014, Spain - Author
Univ Politecn Madrid, Dept Artificial Intelligence, Madrid 28660, Spain - Author
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Abstract

Explainable Artificial Intelligence (XAI) has primarily focused on explaining model predictions, yet a critical gap remains in explaining semantic structure discovery within knowledge graphs derived from concept maps (CMs). This study extends the OBOE (explanatiOns Based On concEpts) framework to address a fundamentally different problem, explainable domain discovery in knowledge graphs (KGs) classification, moving beyond supervised classification to unsupervised structural explanation. Our approach integrates Knowledge Graph Embeddings (KGEs), clustering algorithms, and Large Language Models (LLMs) in a novel triple role-generating structural explanations, verifying hallucinations, and enabling large-scale evaluation. Concept-relation-concept triples are embedded through KGEs and clustered using hierarchical and spectral methods to reveal semantic domains, with QualIT-inspired LLM prompting via Chain-of-Thought reasoning. Evaluation across three corpora (Amazon, BBC News, and Reuters) demonstrated robust classification with mean per-class errors of 0.1, 0.147, and 0.142, and LogLoss values of 0.236, 0.342, and 0.395, discovering 92 semantic domains across 17 topics. Hierarchical clustering achieved superior performance (mean 3.78/5) with higher relevance, while spectral clustering offered better coverage (3.51/5) through more compact structures. By bridging traditional clustering with LLM-based explanation and evaluation, this work establishes a new XAI paradigm for knowledge organization contexts where understanding semantic graph structure is as critical as classification accuracy.
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Keywords

Classification (of information)Clustering algorithmsConcept mapsDomain knowledgeExplainable artificial intelligenceGraph structuresGraph theoryGraphic methodsHierarchical clusteringKnowledge graphKnowledge graphsKnowledge organizationLanguage modelLanguage processingLarge language modelLarge language modelsModeling languagesNatural language processingNatural language processing systemsNatural languagesSemantic similaritySemantic webSemanticsText classificationText processingTopic modeling

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Applied Sciences-Basel due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 50/179, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Multidisciplinary. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Engineering (Miscellaneous).

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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-26:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 5 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/94749/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 16
    • Downloads: 25
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