<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0187-358X</journal-id>
<journal-title><![CDATA[Investigación bibliotecológica]]></journal-title>
<abbrev-journal-title><![CDATA[Investig. bibl]]></abbrev-journal-title>
<issn>0187-358X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Investigaciones Bibliotecológicas y de la Información]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-358X2025000400141</article-id>
<article-id pub-id-type="doi">10.22201/iibi.24488321xe.2025.105.59051</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Propuesta metodológica para la recuperación de información documental: integración de grafos de conocimiento y redes neuronales]]></article-title>
<article-title xml:lang="en"><![CDATA[Methodological Proposal for Document Information Retrieval: Integration of Knowledge Graphs and Neural Networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Polo-Bautista]]></surname>
<given-names><![CDATA[Luis Roberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Casique Vasquez]]></surname>
<given-names><![CDATA[Raquel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional Centro de Investigación en Computación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Politécnico Nacional Escuela Nacional de Biblioteconomía y Archivonomía ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>39</volume>
<numero>105</numero>
<fpage>141</fpage>
<lpage>163</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-358X2025000400141&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-358X2025000400141&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-358X2025000400141&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El uso de grafos para modelar relaciones complejas entre entidades se ha consolidado como una herramienta importante en la recuperación de información documental. De esta forma, el objetivo de este trabajo es proponer una metodología basada en redes neuronales de grafos (GNN) para mejorar la recuperación de información documental mediante grafos de conocimiento (KG). Los documentos se transforman en un grafo de conocimiento construido con lemas y frases nominales sobre el cual se inicializan incrustaciones procesadas con una red de atención gráfica (GAT). Ante una consulta, el sistema extrae un subgrafo del grafo de conocimiento global, ajusta las representaciones y genera respuestas concisas y fácticas. La arquitectura se contrasta con la referencia del gran modelo de lenguaje (LLM) Llama 3.1 usando tres métricas principales: número de tókenes de la respuesta, similitud con el documento de origen y tiempo de procesamiento. Los resultados teóricos y experimentales muestran mejoras en la obtención de respuestas precisas y contextualmente pertinentes.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The use of graphs to model complex relationships between entities has become a valuable tool in document information retrieval. Thus, this work aims to propose a methodology based on graph neural networks (GNNs) to improve document information retrieval using knowledge graphs (KGs). We transformed the documents into a knowledge graph constructed with lemmas and noun-chunks, on which embeddings processed with a graph attention network (GAT) were initialized. When a query is made, the system extracts a subgraph from the global knowledge graph, adjusts the representations, and generates concise and factual responses. We compared its architecture with Llama 3.1, a reference LLM, using three main metrics: number of tokens in the response, similarity to the source document, and processing time. The theoretical and experimental results show improvements due to the accuracy and contextual relevance of the responses obtained.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Recuperación de información]]></kwd>
<kwd lng="es"><![CDATA[Grafos de conocimiento (KG)]]></kwd>
<kwd lng="es"><![CDATA[Red de atención gráfica (GAT)]]></kwd>
<kwd lng="es"><![CDATA[Incrustaciones]]></kwd>
<kwd lng="en"><![CDATA[Information Retrieval]]></kwd>
<kwd lng="en"><![CDATA[Knowledge Graphs (KG)]]></kwd>
<kwd lng="en"><![CDATA[Graph Attention Network (GAT)]]></kwd>
<kwd lng="en"><![CDATA[Embeddings]]></kwd>
</kwd-group>
</article-meta>
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