Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detection

We introduce a next-generation vandalism detection system for Wikidata, one of the largest open-source structured knowledge bases on the Web.Wikidata is highly complex: its items incorporate an ever-expanding universe of factual triples and multilingual texts.While edits can alter both structured and textual content, our approach converts all edits into a single space using a method we call Graph2Text.This allows for evaluating all content changes for potential vandalism using a single multilingual language model.This unified approach improves coverage and simplifies maintenance.Experiments demonstrate that our solution outperforms the current production system.Additionally, we are releasing the code under an open license along with a large dataset of various humangenerated knowledge alterations, enabling further research.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.acl-industry.21
Primary Topic
Wikis in Education and Collaboration
Type
article
Field-Weighted Citation Impact
0.00

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article

Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detection

Mykola Trokhymovych, Lydia Pintscher, Ricardo Baeza‐Yates, Diego Sáez Trumper
Wikis in Education and Collaboration
article

Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detection

Mykola Trokhymovych, Lydia Pintscher, Ricardo Baeza‐Yates, Diego Sáez Trumper
article en

Abstract

We introduce a next-generation vandalism detection system for Wikidata, one of the largest open-source structured knowledge bases on the Web.Wikidata is highly complex: its items incorporate an ever-expanding universe of factual triples and multilingual texts.While edits can alter both structured and textual content, our approach converts all edits into a single space using a method we call Graph2Text.This allows for evaluating all content changes for potential vandalism using a single multilingual language model.This unified approach improves coverage and simplifies maintenance.Experiments demonstrate that our solution outperforms the current production system.Additionally, we are releasing the code under an open license along with a large dataset of various humangenerated knowledge alterations, enabling further research.

Universitat Pompeu Fabra, Agencia Estatal de Investigación
Reduced inequalities
Openalex Percentile: Top 77%
Wikis in Education and Collaboration
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