Cyclical Training Framework with Graph Feature Optimization for Knowledge Graph Reasoning
Source abstract
Abstract
Knowledge graphs (KGs), which organize real-world knowledge in triples, often suffer from issues of incompleteness. To address this, multi-hop knowledge graph reasoning (KGR) methods have been proposed for interpretable knowl... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: Advanced Graph Neural Networks · Cognitive Computing and Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (43)
Show source-provided references
1. Recommendation method for contrastive enhancement of neighborhood information
2. LKPNR: large language models and knowledge graph for personalized news recommendation framework
3. Liu · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 · 2018 · 1 · 2395
4. Moiseev · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022 · 1581
5. Bordes · Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013 · 2013 · 2787
6. Yao L, Mao C, Luo Y. KG-BERT: BERT for knowledge graph completion. arXiv:1909.03193. 2019.
7. Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning
8. Bai · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · 2022 · 164910.18653/v1/2022.emnlp-main.107 ↗
9. Multi-hop knowledge graph reasoning with reward shaping
10. Learning to walk with dual agents for knowledge graph reasoning
11. Path spuriousness-aware reinforcement learning for multi-hop knowledge graph reasoning
12. Chung J, Gülçehre Ç, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555. 2014.
13. BERT: pre-training of deep bidirectional transformers for language understanding
14. Velickovic P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. arXiv:1710.10903. 2017.
15. Convolutional 2D knowledge graph embeddings
16. Complex embeddings for simple link prediction
17. Yichi · Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence · 2023 · 17
18. Exploring the limits of transfer learning with a unified text-to-text transformer
19. Language models are unsupervised multitask learners
20. Wang · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022 · 428110.18653/v1/2022.acl-long.295 ↗
21. Saxena · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022 · 281410.18653/v1/2022.acl-long.201 ↗
22. RotateQVS: representing temporal information as rotations in quaternion vector space for temporal knowledge graph completion
23. Arbitrary time information modeling via polynomial approximation for temporal knowledge graph embedding
24. Simple but effective compound geometric operations for temporal knowledge graph completion
25. DeepPath: a reinforcement learning method for knowledge graph reasoning
26. Wang · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 · 2019 Nov 3–7 · 262310.18653/v1/D19-1264 ↗
27. AInvR: adaptive learning rewards for knowledge graph reasoning using agent trajectories10.26599/TST.2022.9010063 ↗
28. Learning to walk with logical embedding for knowledge reasoning10.1016/j.ins.2024.120471 ↗
29. Decision transformer: reinforcement learning via sequence modeling
30. Double Q-learning
31. Human-level control through deep reinforcement learning10.1038/nature14236 ↗
32. Rainbow: combining improvements in deep reinforcement learning
33. Simple statistical gradient-following algorithms for connectionist reinforcement learning10.1023/A:1022672621406 ↗
34. Schulman · Proceedings of the 32nd International Conference on Machine Learning, ICML 2015 · 2015 Jul 6–11 · 37 · 1889
35. Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O. Proximal policy optimization algorithms. arXiv:1707.06347. 2017.
36. Attention is all you need
37. Knowledge neurons in pretrained transformers
38. Transformer feed-forward layers are key-value memories
39. On Information and Sufficiency10.1214/aoms/1177729694 ↗
40. Observed versus latent features for knowledge base and text inference
41. WordNet: a lexical database for english
42. Chen · Findings of the Association for Computational Linguistics: EMNLP 2020 · 2020 Nov 16–20 · 3227
43. Differentiating concepts and instances for knowledge graph embedding