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Violence against women constitutes a pervasive global human rights violation, demanding innovative approaches to strengthen legal responses. This paper presents the PREJUST4WOMEN project’s contribution: the construction of the first Legal Knowledge Graph (KG) specifically focused on cases of gender-based violence, as adjudicated by the European Court of Human Rights (ECHR). Developed using a bottom-up methodology, the KG is built from ECHR judgments and rigorously structured according to Linked Open Data (LOD) principles. It integrates established legal ontologies and is designed to answer a set of formal competency questions, ensuring domain relevance and practical utility. The resulting knowledge graph is publicly available as FAIR (Findable, Accessible, Interoperable, and Reusable) data, providing an open SPARQL endpoint for advanced querying and analysis. This work fills a significant gap in legal informatics by providing a semantically rich, interoperable resource that facilitates enhanced transparency, supports predictive justice applications, and offers a replicable framework for constructing specialized legal knowledge graphs in other domains.

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A Bottom-Up Framework for Legal Knowledge Graph Construction: A Case Study on Gender-Based Violence

  • 会议论文

  • 发表日期：

    2026年

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  • Claudia d’Amato,
  • Giuseppe Rubini,
  • Fatima Zahra Amara,
  • Nicola Fanizzi

摘要

Violence against women constitutes a pervasive global human rights violation, demanding innovative approaches to strengthen legal responses. This paper presents the PREJUST4WOMEN project’s contribution: the construction of the first Legal Knowledge Graph (KG) specifically focused on cases of gender-based violence, as adjudicated by the European Court of Human Rights (ECHR). Developed using a bottom-up methodology, the KG is built from ECHR judgments and rigorously structured according to Linked Open Data (LOD) principles. It integrates established legal ontologies and is designed to answer a set of formal competency questions, ensuring domain relevance and practical utility. The resulting knowledge graph is publicly available as FAIR (Findable, Accessible, Interoperable, and Reusable) data, providing an open SPARQL endpoint for advanced querying and analysis. This work fills a significant gap in legal informatics by providing a semantically rich, interoperable resource that facilitates enhanced transparency, supports predictive justice applications, and offers a replicable framework for constructing specialized legal knowledge graphs in other domains.