research-article

Detection of Relation Assertion Errors in Knowledge Graphs

Authors:

Heiko PaulheimAuthors Info & Claims

K-CAP '17: Proceedings of the 9th Knowledge Capture Conference

Article No.: 22, Pages 1 - 8

https://doi.org/10.1145/3148011.3148033

Published: 04 December 2017 Publication History

Abstract

Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. We perform an extensive evaluation on a variety of datasets, backed by a manual evaluation on DBpedia and NELL, and we propose and evaluate heuristics for the selection of relevant graph paths to be used as features in our method.

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Cited By

Yu GYe QRuan T(2024)Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic LabelBioengineering10.3390/bioengineering1103022511:3(225)Online publication date: 27-Feb-2024
https://doi.org/10.3390/bioengineering11030225
Carta SGiuliani AManca MPiano LPompianu LTiddia SBoratto LGena CMarras MGermanakos PPopescus E(2024)Towards Knowledge Graph Refinement: Misdirected Triple IdentificationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665235(460-466)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665235
Wan t发仁宋xu Z(2024)Knowledge graph error detection with unsupervised triplet networkSeventh Global Intelligent Industry Conference (GIIC 2024)10.1117/12.3032265(36)Online publication date: 11-Oct-2024
https://doi.org/10.1117/12.3032265
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Index Terms

Detection of Relation Assertion Errors in Knowledge Graphs
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
  2. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Statistical relational learning
2. Information systems
  1. Data management systems
    1. Information integration
      1. Data cleaning

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cover image ACM Conferences

K-CAP '17: Proceedings of the 9th Knowledge Capture Conference

December 2017

271 pages

ISBN:9781450355537

DOI:10.1145/3148011

Copyright © 2017 ACM.

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 ACM 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]

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Published: 04 December 2017

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K-CAP 2017: Knowledge Capture Conference

December 4 - 6, 2017

TX, Austin, USA

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Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Yu GYe QRuan T(2024)Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic LabelBioengineering10.3390/bioengineering1103022511:3(225)Online publication date: 27-Feb-2024
https://doi.org/10.3390/bioengineering11030225
Carta SGiuliani AManca MPiano LPompianu LTiddia SBoratto LGena CMarras MGermanakos PPopescus E(2024)Towards Knowledge Graph Refinement: Misdirected Triple IdentificationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665235(460-466)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665235
Wan t发仁宋xu Z(2024)Knowledge graph error detection with unsupervised triplet networkSeventh Global Intelligent Industry Conference (GIIC 2024)10.1117/12.3032265(36)Online publication date: 11-Oct-2024
https://doi.org/10.1117/12.3032265
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
Zhang QDong JTan QHuang X(2024)Integrating Entity Attributes for Error-Aware Knowledge Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331014936:4(1667-1682)Online publication date: Apr-2024
https://doi.org/10.1109/TKDE.2023.3310149
Zhou YZhu CZhu W(2024)ProMvSDInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10370561:4Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103705
Huang PLiu K(2024)Can data improve knowledge graph?Memetic Computing10.1007/s12293-024-00429-z16:3(403-413)Online publication date: 12-Aug-2024
https://doi.org/10.1007/s12293-024-00429-z
Aisopos FJozashoori SNiazmand EPurohit DRivas ASakor AIglesias EVogiatzis DMenasalvas ERodriguez Gonzalez AVigueras GGomez-Bravo DTorrente MHernández López RProvencio Pulla MDalianis ATriantafillou APaliouras GVidal M(2023)Knowledge graphs for enhancing transparency in health data ecosystems1Semantic Web10.3233/SW-22329414:5(943-976)Online publication date: 8-May-2023
https://doi.org/10.3233/SW-223294
Liu YZhang QDu MHuang XHu X(2023)Error Detection on Knowledge Graphs with Triple Embedding2023 31st European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO58844.2023.10289852(1604-1608)Online publication date: 4-Sep-2023
https://doi.org/10.23919/EUSIPCO58844.2023.10289852
Luo SHan HChang QMiyazaki JHong JLanperne MPark JCerny TShahriar H(2023)Automatic Extraction of Effective Relations in Knowledge Graph for a Recommendation Explanation SystemProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577732(1754-1761)Online publication date: 27-Mar-2023
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