Semantic interlinking of Immigration Data using LLMs for Knowledge Graph Construction

Abstract

The challenge of managing immigration data is exacerbated by its reliance on paper-based, evidence-driven records maintained by legal professionals, creating obstacles for efficient processing and analysis due to inherent trust issues with AI-based systems. This paper introduces a cutting-edge framework to surmount these hurdles by synergizing Large Language Models (LLMs) with Knowledge Graphs (KGs), revolutionizing traditional data handling methods. Our method transforms archaic, paper-based immigration records into a structured, interconnected knowledge network that intricately mirrors the legal and procedural nuances of immigration, ensuring a dynamic and trustworthy platform for data analysis. Utilizing LLMs, we extract vital entities and relationships from diverse legal documents to forge a comprehensive knowledge graph, encapsulating the complex legalities and procedural disparities in immigration processes and mapping the multifaceted interactions among stakeholders like applicants, sponsors, and legal experts. This graph not only facilitates a deep dive into the legal stipulations but also incorporates them, significantly boosting the system’s reliability and precision. With the integration of Retrieval Augmented Generation (RAG) for exact, context-aware data retrieval and Augmented Knowledge Creation for developing a conversational interface via LLMs, our framework offers a scalable, adaptable solution to immigration data management. This innovative amalgamation of LLMs, KGs, and RAG techniques marks a paradigm shift towards more informed, efficient, and trustworthy decision-making in the sphere of global migration, setting a new benchmark for legal technology and data source management.