Identification of Participants of Narratives Using Knowledge Bases

Identifying participants in narratives is important to understand and extract meaning from unstructured texts. This paper investigates the use of DBpedia and Wikifier for this task. We tested these two knowledge base platforms to evaluate their performance in recognizing and extracting entities in Portuguese-language journalistic narrative texts. The results show that both DBpedia and Wikifier present similar results in identifying participants, around 0.40 in the f1-score. The objective of this paper is to study the potential of knowledge bases to improve the understanding of narratives, in addition to suggesting directions for future research in this domain.

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Published
2024-10-14
DOI
https://doi.org/10.5753/sbbd.2024.243103
Primary Topic
Topic Modeling
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article
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article

Identification of Participants of Narratives Using Knowledge Bases

Evelin Amorim, Juliana Machado
Topic Modeling
article

Identification of Participants of Narratives Using Knowledge Bases

Evelin Amorim, Juliana Machado
article en

Abstract

Identifying participants in narratives is important to understand and extract meaning from unstructured texts. This paper investigates the use of DBpedia and Wikifier for this task. We tested these two knowledge base platforms to evaluate their performance in recognizing and extracting entities in Portuguese-language journalistic narrative texts. The results show that both DBpedia and Wikifier present similar results in identifying participants, around 0.40 in the f1-score. The objective of this paper is to study the potential of knowledge bases to improve the understanding of narratives, in addition to suggesting directions for future research in this domain.

Universidade Federal de Santa Catarina (BR), INESC TEC (PT), Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina (BR)
Quality Education
Openalex Percentile: Top 83%
Topic Modeling
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