The content discusses the construction of a legal knowledge graph from Indian judicial documents, emphasizing intellectual property rights cases. By utilizing Graph Neural Networks, the authors aim to predict case similarities and citation links. The study showcases the impact of incorporating domain-specific features extracted through topic modeling and expert inputs on model performance. Furthermore, it explores alternative approaches to improve case similarity predictions and discusses the deployment of a recommendation system for users to explore similar legal cases.
The research delves into the challenges faced by the legal system due to case backlogs and proposes AI tools to automate processes for faster justice delivery. By representing court cases as nodes and citations as edges in a graph, various tasks like link prediction, node similarity, and classification can be facilitated. The study highlights the potential benefits of using legal knowledge graphs for law practitioners to enhance document analysis and find similar cases efficiently.
Through experiments on a dataset of legal documents, the authors compare different models' performance for citation prediction and case similarity tasks. Results indicate that incorporating domain-relevant features improves model accuracy, with LegalBERT encoding enhancing citation prediction but showing minimal impact on case similarity tasks. The discussion also addresses potential alternatives to improve case similarity predictions through unsupervised clustering methods.
The deployment section outlines how the recommendation system is deployed on IBM Cloud, allowing users to explore related cases based on predictions from GNN models. Future work includes leveraging GNN model embeddings for enhanced search capabilities and enabling semantic search using the legal knowledge graph.
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by Jaspreet Sin... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2107.04771.pdfDeeper Inquiries