The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing

Muzhi Li, Minda Hu, Irwin King, Ho-fung Leung


Abstract
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the structural knowledge in the local neighborhood of entities, disregarding semantic knowledge in the textual representations of entities, relations, and types that are also crucial for type inference. Additionally, we observe that the interaction between semantic and structural knowledge can be utilized to address the false-negative problem. In this paper, we propose a novel Semantic and Structure-aware KG Entity Typing (SSET) framework, which is composed of three modules. First, the Semantic Knowledge Encoding module encodes factual knowledge in the KG with a Masked Entity Typing task. Then, the Structural Knowledge Aggregation module aggregates knowledge from the multi-hop neighborhood of entities to infer missing types. Finally, the Unsupervised Type Re-ranking module utilizes the inference results from the two models above to generate type predictions that are robust to false-negative samples. Extensive experiments show that SSET significantly outperforms existing state-of-the-art methods.
Anthology ID:
2024.naacl-long.369
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6625–6638
Language:
URL:
https://aclanthology.org/2024.naacl-long.369
DOI:
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Cite (ACL):
Muzhi Li, Minda Hu, Irwin King, and Ho-fung Leung. 2024. The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6625–6638, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing (Li et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.369.pdf
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