@inproceedings{lourenco-etal-2026-kg,
title = "{KG}-{CRAFT}: Knowledge Graph-based Contrastive Reasoning with {LLM}s for Enhancing Automated Fact-checking",
author = "Louren{\c{c}}o, V{\'i}tor and
Paes, Aline and
Weyde, Tillman and
Depeige, Audrey and
Dubey, Mohnish",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.302/",
pages = "6419--6439",
ISBN = "979-8-89176-380-7",
abstract = "Claim verification is a core module in automated fact-checking systems, tasked with determining claim veracity using retrieved evidence. This work presents KG-CRAFT, a novel knowledge graph-based contrastive reasoning method that enhances automatic claim verification by LLMs. Our approach first constructs a knowledge graph from claims and associated reports, then formulates contextually relevant contrastive questions based on the knowledge graph structure. These questions guide the distillation of evidence-based reports, which are synthesised into a concise summary for veracity assessment. Extensive evaluations on two real-world datasets (LIAR-RAW and RAWFC) demonstrate that our method achieves a new state-of-the-art in predictive performance. Comprehensive analyses validate in detail the effectiveness of our knowledge graph-based contrastive reasoning approach in improving LLMs' fact-checking capabilities."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lourenco-etal-2026-kg">
<titleInfo>
<title>KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking</title>
</titleInfo>
<name type="personal">
<namePart type="given">Vítor</namePart>
<namePart type="family">Lourenço</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aline</namePart>
<namePart type="family">Paes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tillman</namePart>
<namePart type="family">Weyde</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Audrey</namePart>
<namePart type="family">Depeige</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohnish</namePart>
<namePart type="family">Dubey</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Vera</namePart>
<namePart type="family">Demberg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kentaro</namePart>
<namePart type="family">Inui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lluís</namePart>
<namePart type="family">Marquez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Rabat, Morocco</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-380-7</identifier>
</relatedItem>
<abstract>Claim verification is a core module in automated fact-checking systems, tasked with determining claim veracity using retrieved evidence. This work presents KG-CRAFT, a novel knowledge graph-based contrastive reasoning method that enhances automatic claim verification by LLMs. Our approach first constructs a knowledge graph from claims and associated reports, then formulates contextually relevant contrastive questions based on the knowledge graph structure. These questions guide the distillation of evidence-based reports, which are synthesised into a concise summary for veracity assessment. Extensive evaluations on two real-world datasets (LIAR-RAW and RAWFC) demonstrate that our method achieves a new state-of-the-art in predictive performance. Comprehensive analyses validate in detail the effectiveness of our knowledge graph-based contrastive reasoning approach in improving LLMs’ fact-checking capabilities.</abstract>
<identifier type="citekey">lourenco-etal-2026-kg</identifier>
<location>
<url>https://aclanthology.org/2026.eacl-long.302/</url>
</location>
<part>
<date>2026-03</date>
<extent unit="page">
<start>6419</start>
<end>6439</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking
%A Lourenço, Vítor
%A Paes, Aline
%A Weyde, Tillman
%A Depeige, Audrey
%A Dubey, Mohnish
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F lourenco-etal-2026-kg
%X Claim verification is a core module in automated fact-checking systems, tasked with determining claim veracity using retrieved evidence. This work presents KG-CRAFT, a novel knowledge graph-based contrastive reasoning method that enhances automatic claim verification by LLMs. Our approach first constructs a knowledge graph from claims and associated reports, then formulates contextually relevant contrastive questions based on the knowledge graph structure. These questions guide the distillation of evidence-based reports, which are synthesised into a concise summary for veracity assessment. Extensive evaluations on two real-world datasets (LIAR-RAW and RAWFC) demonstrate that our method achieves a new state-of-the-art in predictive performance. Comprehensive analyses validate in detail the effectiveness of our knowledge graph-based contrastive reasoning approach in improving LLMs’ fact-checking capabilities.
%U https://aclanthology.org/2026.eacl-long.302/
%P 6419-6439
Markdown (Informal)
[KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking](https://aclanthology.org/2026.eacl-long.302/) (Lourenço et al., EACL 2026)
ACL