Faculty of Computing

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Author: search.filters.author.Dissanayake, ASubject: search.filters.subject.explainable AI

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    LegalVision: A Knowledge-Driven AI Framework for Legal Understanding and Trust Assessment
    (Institute of Electrical and Electronics Engineers, 2026-06-12) Sharan K; Wicramasinghe D.A.T.N.; Maxwell L.Y; Sivanuja S; Kuruppu, D.S; Dissanayake, A
    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.