<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="vanapalli-etal-2025-unifying">
    <titleInfo>
        <title>Unifying Large Language Models and Knowledge Graphs for efficient Regulatory Information Retrieval and Answer Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kishore</namePart>
        <namePart type="family">Vanapalli</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aravind</namePart>
        <namePart type="family">Kilaru</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Omair</namePart>
        <namePart type="family">Shafiq</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shahzad</namePart>
        <namePart type="family">Khan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-01</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Tuba</namePart>
            <namePart type="family">Gokhan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kexin</namePart>
            <namePart type="family">Wang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ted</namePart>
            <namePart type="family">Briscoe</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, UAE</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">vanapalli-etal-2025-unifying</identifier>
    <location>
        <url>https://aclanthology.org/2025.regnlp-1.4/</url>
    </location>
    <part>
        <date>2025-01</date>
        <extent unit="page">
            <start>22</start>
            <end>30</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Unifying Large Language Models and Knowledge Graphs for efficient Regulatory Information Retrieval and Answer Generation
%A Vanapalli, Kishore
%A Kilaru, Aravind
%A Shafiq, Omair
%A Khan, Shahzad
%Y Gokhan, Tuba
%Y Wang, Kexin
%Y Gurevych, Iryna
%Y Briscoe, Ted
%S Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F vanapalli-etal-2025-unifying
%X 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.
%U https://aclanthology.org/2025.regnlp-1.4/
%P 22-30

Markdown (Informal)

[Unifying Large Language Models and Knowledge Graphs for efficient Regulatory Information Retrieval and Answer Generation](https://aclanthology.org/2025.regnlp-1.4/) (Vanapalli et al., RegNLP 2025)

• Unifying Large Language Models and Knowledge Graphs for efficient Regulatory Information Retrieval and Answer Generation (Vanapalli et al., RegNLP 2025)

ACL

• Kishore Vanapalli, Aravind Kilaru, Omair Shafiq, and Shahzad Khan. 2025. Unifying Large Language Models and Knowledge Graphs for efficient Regulatory Information Retrieval and Answer Generation. In Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025), pages 22–30, Abu Dhabi, UAE. Association for Computational Linguistics.