Sharan KWicramasinghe D.A.T.N.Maxwell L.YSivanuja SKuruppu, D.SDissanayake, A2026-08-192026-06-12979-833154651-9https://rda.sliit.lk/handle/123456789/5250Legal 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.enbias detectionexplainable AIlegal knowledge graphmulti-perspective summarizationrisk classificationproperty lawtrust assessmentConference PaperDOI: 10.1109/CICN70047.2026.11594293