Back to Search
Start Over
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
- Source :
- Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2022
- Publication Year :
- 2022
-
Abstract
- Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs. Tensor-decomposition-based models, such as ComplEx, provide a good trade-off between efficiency and expressiveness, that is crucial because of the large size of real world knowledge graphs. The recent multi-partition embedding interaction (MEI) model subsumes these models by using the block term tensor format and provides a systematic solution for the trade-off. However, MEI has several drawbacks, some of which carried from its subsumed tensor-decomposition-based models. In this paper, we address these drawbacks and introduce the Multi-partition Embedding Interaction iMproved beyond block term format (MEIM) model, with independent core tensor for ensemble effects and soft orthogonality for max-rank mapping, in addition to multi-partition embedding. MEIM improves expressiveness while still being highly efficient, helping it to outperform strong baselines and achieve state-of-the-art results on difficult link prediction benchmarks using fairly small embedding sizes. The source code is released at https://github.com/tranhungnghiep/MEIM-KGE.<br />Comment: Accepted at the International Joint Conference on Artificial Intelligence (IJCAI), 2022; add appendix with extra experiments
- Subjects :
- Computer Science - Artificial Intelligence
Computer Science - Machine Learning
Subjects
Details
- Database :
- arXiv
- Journal :
- Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2022
- Publication Type :
- Report
- Accession number :
- edsarx.2209.15597
- Document Type :
- Working Paper