Knowledge Graphs Construction from Criminal Court Appeals: Insights from the French Cassation Court

• URL: http://arxiv.org/abs/2501.14579v1
• Date: Fri, 24 Jan 2025 15:38:32 GMT
• Title: Knowledge Graphs Construction from Criminal Court Appeals: Insights from the French Cassation Court
• Authors: Alexander V. Belikov, Sacha Raoult,
• Abstract summary: This paper presents a framework for constructing knowledge graphs from appeals to the French Cassation Court. The framework includes a domain-specific ontology and a derived dataset, offering a foundation for structured legal data representation and analysis.
• Score: 49.1574468325115
• License:
• Abstract: Despite growing interest, accurately and reliably representing unstructured data, such as court decisions, in a structured form, remains a challenge. Recent advancements in generative AI applied to language modeling enabled the transformation of text into knowledge graphs, unlocking new opportunities for analysis and modeling. This paper presents a framework for constructing knowledge graphs from appeals to the French Cassation Court. The framework includes a domain-specific ontology and a derived dataset, offering a foundation for structured legal data representation and analysis.

Related papers

• Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models [0.0]
  We propose a novel approach based on knowledge graphs to provide timely access to structured information. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable.
  arXiv  Detail & Related papers  (2024-09-30T07:08:28Z)
• Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries [54.325172923155414]
  We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts.
  arXiv  Detail & Related papers  (2024-09-19T10:38:01Z)
• Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation [5.646219481667151]
  This paper introduces a novel dataset tailored for classification of statements made during police interviews, prior to court proceedings. We introduce a fine-tuned DistilBERT model that achieves state-of-the-art performance in distinguishing truthful from deceptive statements. We also present an XAI interface that empowers both legal professionals and non-specialists to interact with and benefit from our system.
  arXiv  Detail & Related papers  (2024-05-17T11:22:27Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
  We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
  arXiv  Detail & Related papers  (2023-12-18T06:41:23Z)
• Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions [0.16385815610837165]
  The size of legal opinions continues to grow, making it increasingly challenging to develop a model that can accurately predict the rhetorical roles of legal opinions. We propose a novel model architecture for automatically predicting rhetorical roles using pre-trained language models (PLMs) enhanced with knowledge of sentence position information. Based on an annotated corpus from the LegalEval@SemEval2023 competition, we demonstrate that our approach requires fewer parameters, resulting in lower computational costs.
  arXiv  Detail & Related papers  (2023-10-08T20:33:55Z)
• Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs [5.168558598888541]
  This paper presents a knowledge graph construction method for legal case documents and related laws. Our approach consists of three main steps: data crawling, information extraction, and knowledge graph deployment.
  arXiv  Detail & Related papers  (2023-09-16T18:31:47Z)
• Physics of Language Models: Part 1, Learning Hierarchical Language Structures [51.68385617116854]
  Transformer-based language models are effective but complex, and understanding their inner workings is a significant challenge. We introduce a family of synthetic CFGs that produce hierarchical rules, capable of generating lengthy sentences. We demonstrate that generative models like GPT can accurately learn this CFG language and generate sentences based on it.
  arXiv  Detail & Related papers  (2023-05-23T04:28:16Z)
• Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [57.854498238624366]
  We propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP) for data-efficient knowledge graph construction. RAP can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample.
  arXiv  Detail & Related papers  (2022-10-19T16:40:28Z)
• Corpus for Automatic Structuring of Legal Documents [1.8025738207124173]
  We introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. We show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction.
  arXiv  Detail & Related papers  (2022-01-31T11:12:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.