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Cited by 116

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Tucker decomposition-based temporal knowledge graph completion

Shao, Pengpeng, Zhang, Dawei, Yang, Guohua, Tao, Jianhua, Che, Feihu, Liu, Tong·2022·Knowledge-Based Systems
DOI 10.1016/j.knosys.2021.107841
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  • W is interpreted as a high-dimensional linear classifier that can distinguish facts from negative samples.

    “W also is interpreted as a high-dimensional linear classifier that can distinguish facts from negative samples.”

    From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Shao et al., 2021

  • [Shao et al., 2021] adopts Tucker decomposition by regarding a knowledge graph as a 4-way tensor.

    “Similar to CP decomposition TKGC methods, [Shao et al., 2021] adopts the Tucker decomposition by regarding knowledge graph as 4-way tensor.”

    From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Shao et al., 2021

  • [Shao et al., 2021] scores correctness as q(s)=W; e_h, e_r, e_t, e_τ, where W measures interactions among entities, relations and timestamps.

    “Given a fact {h, r, t, τ }, it scores its correctness as q(s) = W; e h , e r , e t , e τ , where W ∈ R de×dr×de×dτ measures the interactions among entities, relations and timestamps.”

    From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Shao et al., 2021

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Citations

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Research works printed but not linked: 31

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  9. B. Yang, S.W.-t. Yih, X. He, J. Gao, L. Deng, Embedding entities and relations for learning and inference in knowledge bases, in: Proceedings of the International Conference on Learning Representations, 2015.
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  12. A. Bordes, N. Usunier, A. Garcia-Durán, J. Weston, O. Yakhnenko, Translating embeddings for modeling multi-relational data, in: Proceedings of the International Conference on Neural Information Processing Systems, 2013, pp. 2787—2795.
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  15. M. Schlichtkrull, T.N. Kipf, P. Bloem, R.v.d. Berg, I. Titov, M. Welling, Modeling relational data with graph convolutional networks, in: Proc. Semant. Web, 2017, pp. 593–607. · DOI · Paper page
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  17. M. Nickel, L. Rosasco, T. Poggio, Holographic embeddings of knowledge graphs, in: Proceedings of AAAI Conference on Artificial Intelligence, 2016, pp. 1955—1961. · DOI · Paper page
  18. T. Jiang, T. Liu, T. Ge, L. Sha, S. Li, B. Chang, Z. Sui, Encoding temporal information for time-aware link prediction, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016, pp. 2350–2354. · DOI
  19. S.S. Dasgupta, S.N. Ray, P. Talukdar, HyTE: Hyperplane-based temporally aware knowledge graph embedding, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018, pp. 2001–2011. · DOI · Paper page
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  23. C. Xu, Y.-Y. Chen, M. Nayyeri, J. Lehmann, Temporal knowledge graph completion using a linear temporal regularizer and multivector embeddings, in: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 2569–2578. · DOI
  24. Y. Xu, H. E, M. Song, W. Song, X. Lv, H. Wang, J. Yang, RTFE: A recursive temporal fact embedding framework for temporal knowledge graph completion, in: NAACL-HLT, 2021. · DOI
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  30. A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. Devito, Z. Lin, A. Desmaison, L. Antiga, A. Lerer, Automatic differentiation in PyTorch, in: Proceedings of the International Conference on Neural Information Processing Systems, 2017.
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Metadata

Authors
Shao, P., Zhang, D., Yang, G., Tao, J., Che, F., & Liu, T.
Venue
Knowledge-Based Systems
Publisher
Elsevier BV
Published
2022-02
Volume
238
Pages
107841
ISSN
0950-7051
DOI
10.1016/j.knosys.2021.107841
Cited by
116
References
3
Language
en
Licence
Funders
  • National Natural Science Foundation of China (61771472, 61773379, 61831022, 61901473)
  • National Key Research and Development Program of China (2017YFC0820602)

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