Fetching the paper…
Reading the bibliography…
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
S. S. Stevens, “On the theory of scales of measurement,” Science , vol. 103, no. 2684, pp. 677–680, 1946
1946
Earlier work this paper cites.
M. Nickel, L. Rosasco, and T. A. Poggio, “Holographic embeddings of knowledge graphs,” in AAAI . AAAI Press, 2016, pp. 1955–1961
1961
Earlier work this paper cites.
L. R. Tucker et al. , “The extension of factor analysis to three-dimensional matrices,” Contributions to mathematical psychology , vol. 110119, 1964
1964
Earlier work this paper cites.
W. W. Denham, “The detection of patterns in alyawara nonverbal behavior,” Ph.D. dissertation, University of Washington, Seattle., 1973
1973
Earlier work this paper cites.
R. J. Rummel, The dimensionality of nations project: attributes of nations and behavior of nations dyads, 1950-1965 . Inter-university Consortium for Political Research, 1976, no. 5409
1976
Earlier work this paper cites.
A. T. McCray, “An upper-level ontology for the biomedical domain,” International Journal of Genomics , vol. 4, no. 1, pp. 80–84, 2003
2003
Earlier work this paper cites.
F. Akrami, M. S. Saeef, Q. Zhang, W. Hu, and C. Li, “Realistic re-evaluation of knowledge graph completion methods: An experimental study,” in SIGMOD Conference . ACM, 2020, pp. 1995–2010
2010
Earlier work this paper cites.
A. Bordes, J. Weston, R. Collobert, and Y. Bengio, “Learning structured embeddings of knowledge bases,” in AAAI . AAAI Press, 2011
2011
Earlier work this paper cites.
M. Nickel, V. Tresp, and H. Kriegel, “A three-way model for collective learning on multi-relational data,” in ICML . Omnipress, 2011, pp. 809–816
2011
Earlier work this paper cites.
L. A. Galárraga, C. Teflioudi, K. Hose, and F. Suchanek, “Amie: association rule mining under incomplete evidence in ontological knowledge bases,” in Proceedings of the 22nd international conference on World Wide Web , 2013, pp. 413–422
2013
Earlier work this paper cites.
A. Bordes, N. Usunier, A. García-Durán, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in NIPS , 2013, pp. 2787–2795
2013
Earlier work this paper cites.
R. Socher, D. Chen, C. D. Manning, and A. Y. Ng, “Reasoning with neural tensor networks for knowledge base completion,” in NIPS , 2013, pp. 926–934
2013
Earlier work this paper cites.
A. Bordes, X. Glorot, J. Weston, and Y. Bengio, “A semantic matching energy function for learning with multi-relational data - application to word-sense disambiguation,” Mach. Learn. , vol. 94, no. 2, pp. 233–259, 2014
2014
Earlier work this paper cites.
Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in AAAI . AAAI Press, 2014, pp. 1112–1119
2014
Earlier work this paper cites.
X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang, “Knowledge vault: a web-scale approach to probabilistic knowledge fusion,” in KDD . ACM, 2014, pp. 601–610
2014
Earlier work this paper cites.
B. Yang, W. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” in ICLR (Poster) , 2015
2015
Earlier work this paper cites.
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in AAAI . AAAI Press, 2015, pp. 2181–2187
2015
Earlier work this paper cites.
G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, “Knowledge graph embedding via dynamic mapping matrix,” in ACL (1) . The Association for Computer Linguistics, 2015, pp. 687–696
2015
Cited alongside, same era.
S. He, K. Liu, G. Ji, and J. Zhao, “Learning to represent knowledge graphs with gaussian embedding,” in CIKM . ACM, 2015, pp. 623–632
2015
Cited alongside, same era.
K. Toutanova and D. Chen, “Observed versus latent features for knowledge base and text inference,” in Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality , 2015, pp. 57–66
2015
Cited alongside, same era.
F. Mahdisoltani, J. Biega, and F. M. Suchanek, “YAGO3: A knowledge base from multilingual wikipedias,” in CIDR . www.cidrdb.org, 2015
2015
Cited alongside, same era.
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, “A review of relational machine learning for knowledge graphs,” Proc. IEEE , vol. 104, no. 1, pp. 11–33, 2016
T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolutional 2d knowledge graph embeddings,” in AAAI . AAAI Press, 2018, pp. 1811–1818
2018
Later among the works it cites.
T. Lacroix, N. Usunier, and G. Obozinski, “Canonical tensor decomposition for knowledge base completion,” in ICML , ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 2018, pp. 2869–2878
2018
Later among the works it cites.
S. K. Mohamed, V. Novácek, P. Vandenbussche, and E. Muñoz, “Loss functions in knowledge graph embedding models,” in DL4KG@ESWC , ser. CEUR Workshop Proceedings, vol. 2377. CEUR-WS.org, 2019, pp. 1–10
2019
Later among the works it cites.
Z. Sun, Z. Deng, J. Nie, and J. Tang, “Rotate: Knowledge graph embedding by relational rotation in complex space,” in ICLR (Poster) . OpenReview.net, 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
T. Rebele, F. M. Suchanek, J. Hoffart, J. Biega, E. Kuzey, and G. Weikum, “YAGO: A multilingual knowledge base from wikipedia, wordnet, and geonames,” in International Semantic Web Conference (2) , ser. Lecture Notes in Computer Science, vol. 9982, 2016, pp. 177–185
2016
Cited alongside, same era.
Q. Wang, Z. Mao, B. Wang, and L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE Trans. Knowl. Data Eng. , vol. 29, no. 12, pp. 2724–2743, 2017
2017
Cited alongside, same era.
R. Kadlec, O. Bajgar, and J. Kleindienst, “Knowledge base completion: Baselines strike back,” in Rep4NLP@ACL . Association for Computational Linguistics, 2017, pp. 69–74
2017
Cited alongside, same era.
2017
Cited alongside, same era.
W. L. Hamilton, R. Ying, and J. Leskovec, “Representation learning on graphs: Methods and applications,” IEEE Data Eng. Bull. , vol. 40, no. 3, pp. 52–74, 2017
2017
Cited alongside, same era.
B. Shi and T. Weninger, “Proje: Embedding projection for knowledge graph completion,” in AAAI . AAAI Press, 2017, pp. 1236–1242
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2019
Later among the works it cites.
S. Zhang, Y. Tay, L. Yao, and Q. Liu, “Quaternion knowledge graph embeddings,” in NeurIPS , 2019, pp. 2731–2741
2019
Later among the works it cites.
I. Balazevic, C. Allen, and T. M. Hospedales, “Tucker: Tensor factorization for knowledge graph completion,” in EMNLP/IJCNLP (1) . Association for Computational Linguistics, 2019, pp. 5184–5193
2019
Later among the works it cites.
2019
Later among the works it cites.
Z. Sun, S. Vashishth, S. Sanyal, P. P. Talukdar, and Y. Yang, “A re-evaluation of knowledge graph completion methods,” in ACL . Association for Computational Linguistics, 2020, pp. 5516–5522
2020
Closest in time.
D. Ruffinelli, S. Broscheit, and R. Gemulla, “You CAN teach an old dog new tricks! on training knowledge graph embeddings,” in ICLR . OpenReview.net, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
S. M. Kazemi, R. Goel, K. Jain, I. Kobyzev, A. Sethi, P. Forsyth, and P. Poupart, “Representation learning for dynamic graphs: A survey,” J. Mach. Learn. Res. , vol. 21, pp. 70:1–70:73, 2020
2020
Closest in time.
R. Wang, B. Li, S. Hu, W. Du, and M. Zhang, “Knowledge graph embedding via graph attenuated attention networks,” IEEE Access , vol. 8, pp. 5212–5224, 2020
2020
Closest in time.
S. Ji, S. Pan, E. Cambria, P. Marttinen, and S. Y. Philip, “A survey on knowledge graphs: Representation, acquisition, and applications,” IEEE Transactions on Neural Networks and Learning Systems , 2021
2021
Closest in time.
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” in ICML , ser. JMLR Workshop and Conference Proceedings, vol. 48. JMLR.org, 2016, pp. 2071–2080
2080
Closest in time.