short-paper

FactCheck: Validating RDF Triples Using Textual Evidence

Authors:

Zafar Habeeb Syed,

Michael Röder,

Axel-Cyrille Ngonga NgomoAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 1599 - 1602

https://doi.org/10.1145/3269206.3269308

Published: 17 October 2018 Publication History

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Abstract

With the increasing uptake of knowledge graphs comes an increasing need for validating the knowledge contained in these graphs. However, the sheer size and number of knowledge bases used in real-world applications makes manual fact checking impractical. In this paper, we employ sentence coherence features gathered from trustworthy source documents to outperform the state of the art in fact checking. Our approach, FactCheck, uses this information to score how likely a fact is to be true and provides the user the evidence used to validate the input facts. We evaluated our approach on two different benchmark datasets and two different corpora. Our results show that FactCheck outperforms the state of the art by up to 13.3% in F-measure and 19.3% AUC. FactCheck is open-source and is available at https://github.com/dice-group/FactCheck.

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Cited By

View all

Ban TWang XChen LWu XChen QChen H(2024)Quality Evaluation of Triples in Knowledge Graph by Incorporating Internal With External ConsistencyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318603335:2(1980-1992)Online publication date: Feb-2024
https://doi.org/10.1109/TNNLS.2022.3186033
Renkhoff JFeng KMeier-Doernberg MVelasquez ASong H(2024)A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial IntelligenceIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33517985:8(3765-3779)Online publication date: Aug-2024
https://doi.org/10.1109/TAI.2024.3351798
Qudus UPekarou FSilva ARöder MNgomo A(2024)FaVEL: Fact Validation Ensemble LearningKnowledge Engineering and Knowledge Management10.1007/978-3-031-77792-9_13(209-225)Online publication date: 20-Nov-2024
https://doi.org/10.1007/978-3-031-77792-9_13
Show More Cited By

Index Terms

FactCheck: Validating RDF Triples Using Textual Evidence
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Document topic models
    2. Search engine architectures and scalability
      1. Distributed retrieval

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Information & Contributors

Information

Published In

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2018

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Short-paper

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Bundesministerium für Verkehr und Digitale Infrastruktur

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CIKM '18

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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

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Ban TWang XChen LWu XChen QChen H(2024)Quality Evaluation of Triples in Knowledge Graph by Incorporating Internal With External ConsistencyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318603335:2(1980-1992)Online publication date: Feb-2024
https://doi.org/10.1109/TNNLS.2022.3186033
Renkhoff JFeng KMeier-Doernberg MVelasquez ASong H(2024)A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial IntelligenceIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33517985:8(3765-3779)Online publication date: Aug-2024
https://doi.org/10.1109/TAI.2024.3351798
Qudus UPekarou FSilva ARöder MNgomo A(2024)FaVEL: Fact Validation Ensemble LearningKnowledge Engineering and Knowledge Management10.1007/978-3-031-77792-9_13(209-225)Online publication date: 20-Nov-2024
https://doi.org/10.1007/978-3-031-77792-9_13
Amaral GRodrigues OSimperl E(2023)ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sourcesSemantic Web10.3233/SW-233467(1-34)Online publication date: 12-Sep-2023
https://doi.org/10.3233/SW-233467
Timmer RMark MKhoo FRibeiro Martins MBerea ARenard GBugbee K(2023)NASA Science Mission Directorate Knowledge Graph DiscoveryCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587585(795-799)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587585
Moghaddam FLehmann JJabeen H(2023)Anomaly Detection for Numerical Literals in Knowledge Graphs: A Short Review of Approaches2023 IEEE Sixth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)10.1109/AIKE59827.2023.00015(46-53)Online publication date: 25-Sep-2023
https://doi.org/10.1109/AIKE59827.2023.00015
Huseynli AAkcayol M(2023)Continuous Knowledge Graph Refinement With Confidence PropagationIEEE Access10.1109/ACCESS.2023.328392511(59226-59237)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3283925
García-Silva ABerrío CGómez-Pérez J(2023)Textual Entailment for Effective Triple Validation in Object PredictionThe Semantic Web – ISWC 202310.1007/978-3-031-47240-4_5(80-100)Online publication date: 27-Oct-2023
https://doi.org/10.1007/978-3-031-47240-4_5
Qudus URöder MKirrane SNgomo A(2023)TemporalFC: A Temporal Fact Checking Approach over Knowledge GraphsThe Semantic Web – ISWC 202310.1007/978-3-031-47240-4_25(465-483)Online publication date: 27-Oct-2023
https://doi.org/10.1007/978-3-031-47240-4_25
Qudus URöder MSaleem MNgonga Ngomo A(2022)HybridFC: A Hybrid Fact-Checking Approach for Knowledge GraphsThe Semantic Web – ISWC 202210.1007/978-3-031-19433-7_27(462-480)Online publication date: 16-Oct-2022
https://doi.org/10.1007/978-3-031-19433-7_27
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