- Title
- Abnormal entity-aware knowledge graph completion
- Creator
- Sun, Ke; Yu, Shuo; Peng, Ciyuan; Li, Xiang; Naseriparsa, Mehdi; Xia, Feng
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190948
- Identifier
- vital:17750
- Identifier
-
https://doi.org/10.1109/ICDMW58026.2022.00118
- Identifier
- ISBN:2375-9232 (ISSN); 9798350346091 (ISBN)
- Abstract
- In real-world scenarios, knowledge graphs remain incomplete and contain abnormal information, such as redundan-cies, contradictions, inconsistencies, misspellings, and abnormal values. These shortcomings in the knowledge graphs potentially affect service quality in many applications. Although many approaches are proposed to perform knowledge graph completion, they are incapable of handling the abnormal information of knowledge graphs. Therefore, to address the abnormal information issue for the knowledge graph completion task, we design a novel knowledge graph completion framework called ABET, which specially focuses on abnormal entities. ABET consists of two components: a) abnormal entity prediction and b) knowledge graph completion. Firstly, the prediction component automati-cally predicts the abnormal entities in knowledge graphs. Then, the completion component effectively captures the heterogeneous structural information and the high-order features of neighbours based on different relations. Experiments demonstrate that ABET is an effective knowledge graph completion framework, which has made significant improvements over baselines. We further verify that ABET is robust for knowledge graph completion task with abnormal entities. © 2022 IEEE.
- Publisher
- IEEE Computer Society
- Relation
- 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022, Orlando USA, 28 November to 1 December 2022, Proceedings: 22nd IEEE International Conference on Data Mining Workshops Vol. 2022-November, p. 891-900
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 IEEE
- Subject
- Abnormal entities; Knowledge graph embedding; Message-passing scheme
- Reviewed
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