LegalVision: A Knowledge-Driven AI Framework for Legal Understanding and Trust Assessment

Abstract

Legal documents are often lengthy, complex, and written in highly technical language, making them difficult for citizens and even legal professionals to interpret efficiently. This creates a need for an intelligent legal support system that can improve accessibility, transparency, and trust in document understanding. This study proposes LegalVision, a knowledgedriven AI framework that integrates multi-perspective legal summarization and visualization, explainable legal reasoning, clause-level bias and risk evaluation, and a dynamic legal knowledge graph for property law documents. This research is conducted within the Sri Lankan legal context using a dataset collected from real Sri Lankan legal documents, including property-related deeds and agreements. The framework processes legal texts through clause segmentation, entity and relation extraction, perspective-based summary generation, infographic visualization, risk classification, and graphsupported reasoning, while preserving links to the original clauses for traceability within a single platform. Therefore, this research contributes a unified and explainable legal AI framework that supports both legal professionals and nonexpert users in understanding property law documents more accurately and efficiently.