research-article

Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data

Authors:

Jiacheng Huang,

Chengfu Huo, and

Weijun RenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

Pages 956 - 965

https://doi.org/10.1145/3485447.3511938

Published: 25 April 2022 Publication History

Abstract

Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this issue, many KG completion methods are proposed. Among them, open KG completion methods leverage the Web to find missing facts. However, noisy data collected from diverse sources may damage the completion accuracy. In this paper, we propose a new trustworthy method that exploits facts for a KG based on multi-sourced noisy data and existing facts in the KG. Specifically, we introduce a graph neural network with a holistic scoring function to judge the plausibility of facts with various value types. We design value alignment networks to resolve the heterogeneity between values and map them to entities even outside the KG. Furthermore, we present a truth inference model that incorporates data source qualities into the fact scoring function, and design a semi-supervised learning way to infer the truths from heterogeneous values. We conduct extensive experiments to compare our method with the state-of-the-arts. The results show that our method achieves superior accuracy not only in completing missing facts but also in discovering new facts.

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Cited By

Yang XWang N(2023)A confidence-aware and path-enhanced convolutional neural network embedding framework on noisy knowledge graphNeurocomputing10.1016/j.neucom.2023.126261545:COnline publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126261
Ge YMa JZhang LLi XLu H(2023)Trustworthiness-aware knowledge graph representation for recommendationKnowledge-Based Systems10.1016/j.knosys.2023.110865278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110865
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: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19433-7_27

Index Terms

Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Alibaba Group
National Natural Science Foundation of China

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Yang XWang N(2023)A confidence-aware and path-enhanced convolutional neural network embedding framework on noisy knowledge graphNeurocomputing10.1016/j.neucom.2023.126261545:COnline publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.126261
Ge YMa JZhang LLi XLu H(2023)Trustworthiness-aware knowledge graph representation for recommendationKnowledge-Based Systems10.1016/j.knosys.2023.110865278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110865
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: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19433-7_27

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