RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion
Linlin Zong, Zhenrong Xie, Chi Ma, Xinyue Liu, Xianchao Zhang, Bo Xu
Abstract
Temporal knowledge graph completion is a critical task within the knowledge graph domain. Existing approaches encompass deep neural network-based methods for temporal knowledge graph embedding and rule-based logical symbolic reasoning. However, the former may not adequately account for structural dependencies between relations.Conversely, the latter methods relies heavily on strict logical rule reasoning and lacks robustness in the face of fuzzy or noisy data. In response to these challenges, we present RENN, a groundbreaking framework that enhances temporal knowledge graph completion through rule embedding. RENN employs a three-step approach. First, it utilizes temporary random walk to extract temporal logic rules. Then, it pre-trains by learning embeddings for each logical rule and its associated relations, thereby enhancing the likelihood of existing quadruples and logical rules. Finally, it incorporates the embeddings of logical rules into the deep neural network. Our methodology has been validated through experiments conducted on various temporal knowledge graph models and datasets, consistently demonstrating its effectiveness and potential in improving temporal knowledge graph completion.- Anthology ID:
- 2024.lrec-main.1215
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 13919–13928
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1215
- DOI:
- Cite (ACL):
- Linlin Zong, Zhenrong Xie, Chi Ma, Xinyue Liu, Xianchao Zhang, and Bo Xu. 2024. RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13919–13928, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion (Zong et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1215.pdf