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This paper introduces a microservices-based architecture designed for executing complex linguistic tasks using Large Language Models (LLMs) and Knowledge Graphs (KGs). It has been conceived by focusing on the legal domain, and it integrates Domain-specific KGs and Constraint KGs to address tasks such as law extraction and reasoning. We outline how the pipeline works through a running example involving the extraction of legislative references from legal documents. Furthermore, we discuss a methodology for building KGs from unstructured documents and employing zero-shot prompt engineering techniques to facilitate information extraction. Finally, we present a validation process leveraging the Constraint KG to ensure the coherence and correctness of generated outputs.

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A Service-Based Pipeline for Complex Linguistic Tasks Adopting LLMs and Knowledge Graphs

  • 会议论文

  • 发表日期：

    2025年

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  • Filippo Bianchini,
  • Marco Calamo,
  • Francesca De Luzi,
  • Mattia Macrì,
  • Massimo Mecella

摘要

This paper introduces a microservices-based architecture designed for executing complex linguistic tasks using Large Language Models (LLMs) and Knowledge Graphs (KGs). It has been conceived by focusing on the legal domain, and it integrates Domain-specific KGs and Constraint KGs to address tasks such as law extraction and reasoning. We outline how the pipeline works through a running example involving the extraction of legislative references from legal documents. Furthermore, we discuss a methodology for building KGs from unstructured documents and employing zero-shot prompt engineering techniques to facilitate information extraction. Finally, we present a validation process leveraging the Constraint KG to ensure the coherence and correctness of generated outputs.