The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
InteractE: Improving Convolution-Based Knowledge Graph Embeddings by Increasing Feature Interactions
Open accessAbstract
Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generate low-dimensional embeddings of entities and relations, and use these to make inferences. ConvE, a recently proposed approach, applies convolutional filters on 2D reshapings of entity and relation embeddings in order to capture rich interactions between their components. However, the number of interactions that ConvE can capture is limited. In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE. InteractE is based on three key ideas – feature permutation, a novel feature reshaping, and circular convolution. Through extensive experiments, we find that InteractE outperforms state-of-the-art convolutional link prediction baselines on FB15k-237. Further, InteractE achieves an MRR score that is 9%, 7.5%, and 23% better than ConvE on the FB15k-237, WN18RR and YAGO3-10 datasets respectively. The results validate our central hypothesis – that increasing feature interaction is beneficial to link prediction performance. We make the source code of InteractE available to encourage reproducible research.
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Cited by
Knowledge Graphs: Opportunities and Challenges
Peng, C., Xia, F., Naseriparsa, M., & Osborne, F. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10465-9 · Cited by 717
(2020) · doi:10.18653/v1/2020.acl-main.412 · Cited by 542
A Survey on Knowledge Graph Embeddings for Link Prediction
Wang, M., Qiu, L., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13030485 · Cited by 212
Question Answering Over Temporal Knowledge Graphs
Saxena, A., Chakrabarti, S., & Talukdar, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.520 · Cited by 91
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Metadata
- Authors
- Vashishth, S., Sanyal, S., Nitin, V., Agrawal, N., & Talukdar, P.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 03
- Pages
- 3009-3016
- ISSN
- 2374-3468
- 2159-5399
- OpenAlex
- W2997837621
- Cited by
- 365
- References
- 31
- FWCI
- 20.17
- Type
- journal article
- Language
- en
- Open access
- green
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- Vashishth, S., Sanyal, S., Nitin, V., Agrawal, N., & Talukdar, P. (2020). InteractE: Improving Convolution-Based Knowledge Graph Embeddings by Increasing Feature Interactions. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v34i03.5694
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