Omair Shafiq


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2025

pdf bib
Unifying Large Language Models and Knowledge Graphs for efficient Regulatory Information Retrieval and Answer Generation
Kishore Vanapalli | Aravind Kilaru | Omair Shafiq | Shahzad Khan
Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)

In a rapidly changing socio-economic land-scape, regulatory documents play a pivotal role in shaping responses to emerging challenges. An efficient regulatory document monitoring system is crucial for addressing the complexi ties of a dynamically evolving world, enabling prompt crisis response, simplifying compliance, and empowering data-driven decision-making. In this work, we present a novel comprehensive analytical framework, PolicyInsight, which is based on a specialized regulatory data model and state-of-the-art NLP techniques of Large Language Models (LLMs) and Knowledge Graphs to derive timely insights, facilitating data-driven decision-making and fostering a more transparent and informed governance ecosystem for regulators, businesses, and citizens.