Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation (2306.14580v2)

Published 26 Jun 2023 in cs.CL and cs.AI

Abstract: This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at~\url{https://github.com/dellixx/TransERR}.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. Tucker: Tensor factorization for knowledge graph completion. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5185–5194.
  2. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 1247–1250.
  3. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26.
  4. Pairre: Knowledge graph embeddings via paired relation vectors. In 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), pages 4360–4369.
  5. Bidirectional attentive memory networks for question answering over knowledge bases. arXiv preprint arXiv:1903.02188.
  6. Convolutional 2d knowledge graph embeddings. In Thirty-second AAAI conference on artificial intelligence.
  7. Improving knowledge graph embedding using simple constraints. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 110–121.
  8. Improved knowledge graph embedding using background taxonomic information. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 3526–3533.
  9. Rotate3d: Representing relations as rotations in three-dimensional space for knowledge graph embedding. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 385–394.
  10. Wm R Hamilton. 1844. Theory of quaternions. Proceedings of the Royal Irish Academy (1836-1869), 3:1–16.
  11. Openke: An open toolkit for knowledge embedding. In Proceedings of EMNLP.
  12. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118–22133.
  13. How knowledge graph and attention help? a quantitative analysis into bag-level relation extraction. arXiv preprint arXiv:2107.12064.
  14. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 687–696.
  15. Knowledge graph completion with adaptive sparse transfer matrix. In Thirtieth AAAI conference on artificial intelligence.
  16. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2):494–514.
  17. Knowledge graph-based legal search over german court cases. In European Semantic Web Conference, pages 293–297. Springer.
  18. Seyed Mehran Kazemi and David Poole. 2018. Simple embedding for link prediction in knowledge graphs. Advances in neural information processing systems, 31.
  19. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  20. Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia. Semantic web, 6(2):167–195.
  21. Quatse: Spherical linear interpolation of quaternion for knowledge graph embeddings. In CCF International Conference on Natural Language Processing and Chinese Computing, pages 209–220. Springer.
  22. TranSHER: Translating knowledge graph embedding with hyper-ellipsoidal restriction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8517–8528, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  23. Learning entity and relation embeddings for knowledge graph completion. In Twenty-ninth AAAI conference on artificial intelligence.
  24. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
  25. George A Miller. 1995. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39–41.
  26. Never-ending learning. Communications of the ACM, 61(5):103–115.
  27. Quatre: Relation-aware quaternions for knowledge graph embeddings. In Companion Proceedings of the Web Conference 2022, pages 189–192.
  28. A novel embedding model for knowledge base completion based on convolutional neural network. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 327–333.
  29. A three-way model for collective learning on multi-relational data. In Icml.
  30. Modeling relational data with graph convolutional networks. In European semantic web conference, pages 593–607. Springer.
  31. Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web, pages 697–706.
  32. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197.
  33. Kristina Toutanova and Danqi Chen. 2015. Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd workshop on continuous vector space models and their compositionality, pages 57–66.
  34. Complex embeddings for simple link prediction. In International conference on machine learning, pages 2071–2080. PMLR.
  35. Interacte: Improving convolution-based knowledge graph embeddings by increasing feature interactions. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3009–3016.
  36. Denny Vrandečić and Markus Krötzsch. 2014. Wikidata: A free collaborative knowledgebase. Commun. ACM, 57(10):78–85.
  37. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12):2724–2743.
  38. Knowledge base completion using embeddings and rules. In Twenty-fourth international joint conference on artificial intelligence.
  39. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 28.
  40. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575.
  41. Transms: Knowledge graph embedding for complex relations by multidirectional semantics. In IJCAI, pages 1935–1942.
  42. Triplere: Knowledge graph embeddings via tripled relation vectors. arXiv preprint arXiv:2209.08271.
  43. Quaternion knowledge graph embeddings. Advances in neural information processing systems, 32.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now