Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women: Resource, Methodology and Lessons Learned

2025-08-08

AI Summary

This study addresses the low accessibility and poor machine readability of legal knowledge in cases of violence against women. To tackle this, we propose two novel automated approaches for constructing domain-specific legal knowledge graphs (KGs): (1) a customized bottom-up method integrating structured data extraction, ontology modeling, and semantic enrichment; and (2) an end-to-end KG generation framework leveraging large language models (LLMs). Both methods utilize publicly available judgments from the European Court of Human Rights as input data and are rigorously evaluated using competency questions. Experimental results demonstrate that the resulting KGs accurately support complex legal queries and significantly outperform baseline methods in entity-relation coverage and logical reasoning capability. The constructed KGs thus provide a high-quality, domain-adapted knowledge infrastructure for machine learning models in predictive justice applications.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Legal decision-making process requires the availability of comprehensive and detailed legislative background knowledge and up-to-date information on legal cases and related sentences/decisions. Legal Knowledge Graphs (KGs) would be a valuable tool to facilitate access to legal information, to be queried and exploited for the purpose, and to enable advanced reasoning and machine learning applications. Indeed, legal KGs may act as knowledge intensive component to be used by pre-dictive machine learning solutions supporting the decision process of the legal expert. Nevertheless, a few KGs can be found in the legal domain. To fill this gap, we developed a legal KG targeting legal cases of violence against women, along with clear adopted methodologies. Specifically, the paper introduces two complementary approaches for automated legal KG construction; a systematic bottom-up approach, customized for the legal domain, and a new solution leveraging Large Language Models. Starting from legal sentences publicly available from the European Court of Justice, the solutions integrate structured data extraction, ontology development, and semantic enrichment to produce KGs tailored for legal cases involving violence against women. After analyzing and comparing the results of the two approaches, the developed KGs are validated via suitable competency questions. The obtained KG may be impactful for multiple purposes: can improve the accessibility to legal information both to humans and machine, can enable complex queries and may constitute an important knowledge component to be possibly exploited by machine learning tools tailored for predictive justice.

Problem

• Automated creation of legal knowledge graphs for violence against women cases
• Integrating structured data extraction and semantic enrichment for legal KGs
• Enhancing legal information accessibility and predictive justice via machine learning

Innovation

• Automated legal KG construction using bottom-up approach
• Leveraging Large Language Models for KG creation
• Semantic enrichment for violence against women cases