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A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2405.18106v1)

Published 28 May 2024 in cs.AI

Abstract: Temporal knowledge graph (TKG) reasoning has two settings: interpolation reasoning and extrapolation reasoning. Both of them draw plenty of research interest and have great significance. Methods of the former de-emphasize the temporal correlations among facts sequences, while methods of the latter require strict chronological order of knowledge and ignore inferring clues provided by missing facts of the past. These limit the practicability of TKG applications as almost all of the existing TKG reasoning methods are designed specifically to address either one setting. To this end, this paper proposes an original Temporal PAth-based Reasoning (TPAR) model for both the interpolation and extrapolation reasoning. TPAR performs a neural-driven symbolic reasoning fashion that is robust to ambiguous and noisy temporal data and with fine interpretability as well. Comprehensive experiments show that TPAR outperforms SOTA methods on the link prediction task for both the interpolation and the extrapolation settings. A novel pipeline experimental setting is designed to evaluate the performances of SOTA combinations and the proposed TPAR towards interpolation and extrapolation reasoning. More diverse experiments are conducted to show the robustness and interpretability of TPAR.

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References (59)
  1. John S. Baras and George Theodorakopoulos. 2010. Path Problems in Networks. Synthesis Lectures on Communication Networks. Morgan & Claypool Publishers.
  2. Richard Bellman. 1958. On a routing problem. Quarterly of Applied Mathematics, 16:87–90.
  3. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, pages 1247–1250.
  4. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, pages 2787–2795.
  5. Rotateqvs: Representing temporal information as rotations in quaternion vector space for temporal knowledge graph completion. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pages 5843–5857. Association for Computational Linguistics.
  6. Hyte: Hyperplane-based temporally aware knowledge graph embedding. In Proceedings of the 2018 conference on empirical methods in natural language processing, pages 2001–2011.
  7. Convolutional 2d knowledge graph embeddings. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 1811–1818.
  8. Introducing wikidata to the linked data web. In The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I, volume 8796 of Lecture Notes in Computer Science, pages 50–65. Springer.
  9. Lester Randolph Ford. 1956. Network flow theory.
  10. Learning sequence encoders for temporal knowledge graph completion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pages 4816–4821. Association for Computational Linguistics.
  11. Diachronic embedding for temporal knowledge graph completion. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04):3988–3995.
  12. Jointly embedding knowledge graphs and logical rules. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, pages 192–202. The Association for Computational Linguistics.
  13. Knowledge graph embedding with iterative guidance from soft rules. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 4816–4823. AAAI Press.
  14. Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 7301–7316. Association for Computational Linguistics.
  15. Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net.
  16. Learning neural ordinary equations for forecasting future links on temporal knowledge graphs. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pages 8352–8364. Association for Computational Linguistics.
  17. Kurt Hornik. 1991. Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2):251–257.
  18. Temporal knowledge base completion: New algorithms and evaluation protocols. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pages 3733–3747. Association for Computational Linguistics.
  19. Recurrent event network: Autoregressive structure inference over temporal knowledge graphs. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6669–6683.
  20. Learning to walk across time for interpretable temporal knowledge graph completion. In KDD ’21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021, pages 786–795. ACM.
  21. Andrey V. Korotayev and Sergey V. Tsirel. 2010. A spectral analysis of world gdp dynamics: Kondratieff waves, kuznets swings, juglar and kitchin cycles in global economic development, and the 2008c2009 economic crisis. Structure and Dynamics: eJournal of Anthropological and Related Sciences, pages 1–57.
  22. Tensor decompositions for temporal knowledge base completion. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net.
  23. Canonical tensor decomposition for knowledge base completion. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 2869–2878. PMLR.
  24. Relational retrieval using a combination of path-constrained random walks. Mach. Learn., 81(1):53–67.
  25. Reading the web with learned syntactic-semantic inference rules. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, July 12-14, 2012, Jeju Island, Korea, pages 1017–1026. ACL.
  26. Icews events and aggregations. Harvard Dataverse, 3.
  27. Julien Leblay and Melisachew Wudage Chekol. 2018. Deriving validity time in knowledge graph. In Companion Proceedings of the The Web Conference 2018, pages 1771–1776.
  28. Tirgn: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pages 2152–2158. ijcai.org.
  29. Complex evolutional pattern learning for temporal knowledge graph reasoning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pages 290–296. Association for Computational Linguistics.
  30. Temporal knowledge graph reasoning based on evolutional representation learning. In SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021, pages 408–417. ACM.
  31. Learn from relational correlations and periodic events for temporal knowledge graph reasoning. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, pages 1559–1568. ACM.
  32. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, pages 2181–2187. AAAI Press.
  33. Tlogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pages 4120–4127. AAAI Press.
  34. YAGO3: A knowledge base from multilingual wikipedias. In CIDR 2015, Seventh Biennial Conference on Innovative Data Systems Research.
  35. Compositional vector space models for knowledge base inference. In 2015 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 22-25, 2015. AAAI Press.
  36. Meng Qu and Jian Tang. 2019. Probabilistic logic neural networks for reasoning. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 7710–7720.
  37. Problog: A probabilistic prolog and its application in link discovery. In IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007, pages 2462–2467.
  38. Matthew Richardson and Pedro M. Domingos. 2006. Markov logic networks. Mach. Learn., 62(1-2):107–136.
  39. Modeling relational data with graph convolutional networks. In The Semantic Web - 15th International Conference, ESWC 2018, volume 10843, pages 593–607.
  40. End-to-end structure-aware convolutional networks for knowledge base completion. In The Thirty-Third AAAI Conference on Artificial Intelligence, pages 3060–3067.
  41. A novel encoder-decoder knowledge graph completion model for robot brain. Frontiers Neurorobotics, 15:674428.
  42. Causal attention for interpretable and generalizable graph classification. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022, pages 1696–1705. ACM.
  43. Timetraveler: Reinforcement learning for temporal knowledge graph forecasting. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pages 8306–8319. Association for Computational Linguistics.
  44. Complex embeddings for simple link prediction. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, volume 48 of JMLR Workshop and Conference Proceedings, pages 2071–2080.
  45. Composition-based multi-relational graph convolutional networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net.
  46. Graph attention networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.
  47. Logic rules powered knowledge graph embedding. CoRR, abs/1903.03772.
  48. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada, pages 1112–1119. AAAI Press.
  49. Temporal knowledge graph completion using a linear temporal regularizer and multivector embeddings. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pages 2569–2578. Association for Computational Linguistics.
  50. Temporal knowledge graph embedding model based on additive time series decomposition. CoRR, abs/1911.07893.
  51. TeRo: A time-aware knowledge graph embedding via temporal rotation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1583–1593, Barcelona, Spain (Online). International Committee on Computational Linguistics.
  52. Geometric algebra based embeddings for static and temporal knowledge graph completion. IEEE Trans. Knowl. Data Eng., 35(5):4838–4851.
  53. Embedding entities and relations for learning and inference in knowledge bases. In 3rd International Conference on Learning Representations, ICLR 2015.
  54. Matthew D. Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I, volume 8689 of Lecture Notes in Computer Science, pages 818–833. Springer.
  55. Neural, symbolic and neural-symbolic reasoning on knowledge graphs. AI Open, 2:14–35.
  56. Yongqi Zhang and Quanming Yao. 2022. Knowledge graph reasoning with relational digraph. In WWW ’22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022, pages 912–924. ACM.
  57. Target relational attention-oriented knowledge graph reasoning. Neurocomputing, 461:577–586.
  58. Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pages 4732–4740. AAAI Press.
  59. Neural bellman-ford networks: A general graph neural network framework for link prediction. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 29476–29490.
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