- Title
- Duet Representation Learning with Entity Multi-attribute Information in Knowledge Graphs
- Creator
- Xu, Yuanbo; Zhang, Yuanbo; Yang, Yongjian; Xu, Hangtong; Yue, Lin
- Relation
- 19th International Conference on Advanced Data Mining and Applications (ADMA 2023). Proceedings of the 19th International Conference on Advanced Data Mining and Applications (ADMA 2023) (Shenyang, China 21-23 August, 2023) p. 32-45
- Publisher Link
- http://dx.doi.org/10.1007/978-3-031-46664-9_3
- Publisher
- Springer
- Resource Type
- conference paper
- Date
- 2023
- Description
- Representation Learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimension space. Most methods concentrate on learning entities’ representations with structure information indicating the relations between entities (Trans- methods), while the utilization of entity multi-attribute information is insufficient for some scenarios, such as cold start issues or zero-shot problems. How to utilize the complex and diverse multi-attribute information for RL is still a challenging problem for enhancing knowledge graph embedding research. In this paper, we propose a novel RL model Duet Entity Representation Learning (DERL) for knowledge graphs, which takes advantage of entity multi-attribute information. Specifically, we devise a novel encoder Entity Attribute Encoder (EAE), which encodes both entity attribute types and values to generate the entities’ attribute-based representations. We further learn the entities’ representations with both structure information and multi-attribute information in DERL. We evaluate our method on two tasks: the knowledge graph completion task and the zero-shot task. Experimental results on real-world datasets show that our method outperforms other baselines on two downstream tasks by building effective representations for entities from their multi-attribute information. The source code of this paper can be obtained from https://anonymous.4open.science/r/DUET-adma2023/.
- Subject
- multi-attribute; Representation Learning; knowledge graphs; zero-shot task
- Identifier
- http://hdl.handle.net/1959.13/1496011
- Identifier
- uon:54109
- Identifier
- ISBN:9783031466632
- Language
- eng
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