Unifying Large Language Models and Knowledge Graphs for efficient Regulatory Information Retrieval and Answer Generation (2025)

Abstract: 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.

## introduction 1.problem statement regulatory policy monitoring @xcite refers to the systematic process of observing, tracking, and analyzing the policies and regulations established by regulatory bodies. the primary goal is to stay informed about any changes, updates, or new developments in regulatory policies that may affect various sectors, industries, or the general public. this monitoring process involves continuous observation, change detection, impact analysis, and compliance monitoring. ## overall architecture in this section, we first provide an overview of pol-icyinsight's high-level architecture. then, we dive into the main design decisions in the framework. ## implementation paradigm the overall structure of policyinsight and the connections between different modules are illustrated in figure 4 . in this section, we discuss the detailed implementation of three main components of policyinsight: the monitoring subsystem, streaming data management subsystem, and the threelayered monitoring subsystem. we show how to combine different technologies to achieve a highperformance data-analytics system for policyinsight. ## conclusions in conclusion, our work introduces policyinsight, a novel analytical framework designed to address the evolving challenges of regulatory document monitoring in a rapidly changing socio-economic landscape. by leveraging a sophisticated policy data model and state-of-the-art nlp and knowledge graph techniques in a combined fashion, poli-cyinsight enables stakeholders to continuous monitoring and derive timely insights from policy documents, fostering data-driven decision-making. incorporating a novel dynamic policy data model for a scalable and efficient knowledge graph, policyin-sight leverages an innovative unified approach to combining capabilities of both llms and kgs to achieve remarkable accuracy for policy deduplication, policy impact analysis and policy changes prediction. by providing stakeholders with access to actionable insights derived from policy data, policyinsight empowers policymakers, businesses, and citizens to make informed decisions, respond effectively to crises, and comply with regulatory requirements. looking ahead, the continued refinement and expansion of policyinsight holds immense potential for driving positive change in governance practices. future research endeavors may focus on enhancing the scalability, interoperability, and predictive capabilities of policyinsight, thereby enabling stakeholders to anticipate regulatory changes, identify emerging trends, and proactively address societal challenges. our future work also addresses the few remaining items from the framework. in summary, policyinsight stands at the forefront of innovation in policy monitoring and analysis, offering a powerful tool for navigating the complexities of the modern regulatory landscape and fostering a more transparent, informed, and responsive governance ecosystem.