Analysis of Narrative Networks in Spanish Literature: An Automatic Learning Method for Knowledge Graphs
DOI:
https://doi.org/10.56294/mr2025226Keywords:
Digital literary, knowledge graphs (KG), Graph Convolutional Networks (GCNs), Spanish literature, entity-relationalAbstract
Introduction: Spanish literature, known for its emotional depth and complex character interactions, has seen limited computational exploration of these relationships. The lack of annotated data and NLP tools for Spanish hampers the development of accurate knowledge graphs (KGs) to map character dynamics.
Objective:This research presents an automated pipeline to extract, organize, and visualize character relationships in Spanish literary classics, such as Don Quijote, La Regenta, and Fortunata y Jacinta, with over 2,500 entity interactions.
Method: The method leverages multilingual contextual embeddings for high-accuracy inferences, using Multilingual Bidirectional Encoder Representations from Transformers (mBERT) for feature extraction and Named Entity Recognition (NER) for character identification. Graph Convolutional Networks (GCNs) are employed to capture narrative ties through joint entity-relational learning, and the KG is built with RDF triples and visualized using SpaCy.
Results: The approach achieves significant performance metrics: precision (85.01%), recall (87.06%), and F1 score (87.96%). The generated networks effectively represent character interactions and narrative structure, offering valuable insights into the relational dynamics of the texts.
Conclusions: This method contributes to the development of high-quality KGs for Spanish literature, advancing comparative storytelling, computational literary research, and understanding character networks in literary analysis.
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