Error Detection on Knowledge Graphs with Triple Embedding

TY - GEN

T1 - Error Detection on Knowledge Graphs with Triple Embedding

AU - Liu, Yezi

AU - Zhang, Qinggang

AU - Du, Mengnan

AU - Huang, Xiao

AU - Hu, Xia

N1 - Publisher Copyright: © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

PY - 2023

Y1 - 2023

N2 - Knowledge graphs (KGs), as essential ingredients in many real-world applications, always contain a considerable number of errors. KG error detection aims to find the triples whose head entity, tail entity, and corresponding relation are mismatched. Despite being urgently needed, existing KG error detection methods lack generalizability. They mainly utilize supervised information such as entity type or erroneous labels, but such information is not often available in the real world. The challenges in detecting errors in KGs are twofold. Firstly, KGs exhibit unique data characteristics that distinguish them from general graphs. Secondly, real-world KGs tend to be large, and labels are often scarce. To bridge the gap, we propose a novel KG error detection framework based on triple embedding, termed TripleNet. TripleNet constructs a triple network by treating each triple as a node and connecting them via shared entities. It then employs a Bi-LSTM module to capture intra-triple translational information at the local level and uses a graph attention network to gather inter-triple contextual information at the global level. Finally, it computes the suspicious score of each triple by integrating its local and global-level information. Experimental results on two real-world KGs demonstrated that TripleNet outperforms state-of-the-art error detection algorithms with comparable or even better efficiency.

AB - Knowledge graphs (KGs), as essential ingredients in many real-world applications, always contain a considerable number of errors. KG error detection aims to find the triples whose head entity, tail entity, and corresponding relation are mismatched. Despite being urgently needed, existing KG error detection methods lack generalizability. They mainly utilize supervised information such as entity type or erroneous labels, but such information is not often available in the real world. The challenges in detecting errors in KGs are twofold. Firstly, KGs exhibit unique data characteristics that distinguish them from general graphs. Secondly, real-world KGs tend to be large, and labels are often scarce. To bridge the gap, we propose a novel KG error detection framework based on triple embedding, termed TripleNet. TripleNet constructs a triple network by treating each triple as a node and connecting them via shared entities. It then employs a Bi-LSTM module to capture intra-triple translational information at the local level and uses a graph attention network to gather inter-triple contextual information at the global level. Finally, it computes the suspicious score of each triple by integrating its local and global-level information. Experimental results on two real-world KGs demonstrated that TripleNet outperforms state-of-the-art error detection algorithms with comparable or even better efficiency.

UR - http://www.scopus.com/inward/record.url?scp=85178377708&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85178377708&partnerID=8YFLogxK

U2 - 10.23919/EUSIPCO58844.2023.10289852

DO - 10.23919/EUSIPCO58844.2023.10289852

M3 - Conference contribution

T3 - European Signal Processing Conference

SP - 1604

EP - 1608

BT - 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings

PB - European Signal Processing Conference, EUSIPCO

T2 - 31st European Signal Processing Conference, EUSIPCO 2023

Y2 - 4 September 2023 through 8 September 2023

ER -