References¶

Unimodal KGE Models.

balazevic2019: Balažević, et al. (2019) TuckER: Tensor Factorization for Knowledge Graph Completion. EMNLP’19
bordes2011: Bordes, A., et al. (2011). Learning Structured Embeddings of Knowledge Bases. AAAI. Vol. 6. No. 1.
bordes2013: Bordes, A., et al. (2013). Translating embeddings for modeling multi-relational data. NIPS.
bordes2014: Bordes, A., et al. (2014). A semantic matching energy function for learning with multi-relational data. Machine
dettmers2018: Dettmers, T., et al. (2018) Convolutional 2d knowledge graph embeddings. Thirty-Second AAAI Conference on Artificial Intelligence.
dong2014: Dong, X., et al. (2014) Knowledge vault: A web-scale approach to probabilistic knowledge fusion. ACM.
ji2015: Ji, G., et al. (2015). Knowledge graph embedding via dynamic mapping matrix. ACL.
kazemi2018: Kazemi, S.M. and Poole, D. (2018). SimplE Embedding for Link Prediction in Knowledge Graphs. NIPS’18
he2015: Shizhu, H., et al. (2017). Learning to Represent Knowledge Graphs with Gaussian Embedding. CIKM’17.
lin2015: Lin, Y., et al. (2015). Learning entity and relation embeddings for knowledge graph completion. AAAI. Vol. 15.
nguyen2018: Nguyen, D. Q., et al (2018) A Novel Embedding Model for Knowledge Base CompletionBased on Convolutional Neural Network. NAACL-HLT 2018
nickel2011: Nickel, M., et al. (2011) A Three-Way Model for Collective Learning on Multi-Relational Data. ICML. Vol. 11.
nickel2016: Nickel, M. et al. (2016) Holographic Embeddings of Knowledge Graphs. AAAI 2016.
schlichtkrull2018: Schlichtkrull, M., et al. (2018) Modeling relational data with graph convolutional networks. ESWC’18.
sharifzadeh2019: Sharifzadeh et al. (2019) Extension of ERMLP in PyKEEN.
shi2017: Shi, B., and Weninger, T. ProjE: Embedding Projection for Knowledge Graph Completion, AAAI 2017
sun2019: Sun, Z., et al. (2019) RotatE: Knowledge Graph Embeddings by relational rotation in complex space. ICLR 2019.
trouillon2016: Trouillon, T., et al. (2016) Complex embeddings for simple link prediction. International Conference on Machine Learning. 2016.
wang2014: Wang, Z., et al. (2014). Knowledge Graph Embedding by Translating on Hyperplanes. AAAI. Vol. 14.
yang2014: Yang, B., et al. (2014). Embedding Entities and Relations for Learning and Inference in Knowledge Bases. CoRR, abs/1412.6575.
socher2013: Socher, R., et al. (2013) Reasoning with neural tensor networks for knowledge base completion.. NIPS. 2013.

Multimodal KGE Models.

kristiadi2018: Kristiadi, A.., et al. (2018) Incorporating literals into knowledge graph embeddings.. arXiv, 1802.00934.
ali2020a: Ali, M., et al. (2020). Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework. arXiv, 2006.13365.
safavi2020: Safavi, T. & Koutra, D. (2020). CoDEx: A Comprehensive Knowledge Graph Completion Benchmark. arXiv, 2009.07810.
shi2017b: Shi, B., & Weninger, T. (2017). Open-World Knowledge Graph Completion. arXiv, 1957–1964.
santos2020: Santos, A., et al (2020). Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making. bioRxiv, 2020.05.09.084897.
speer2017: Robyn Speer, Joshua Chin, and Catherine Havasi. (2017) ConceptNet 5.5: An Open Multilingual Graph of General Knowledge. In proceedings of AAAI 31.
breit2020: Breit, A., et al (2020). OpenBioLink: A benchmarking framework for large-scale biomedical link prediction, Bioinformatics
ilievski2020: Ilievski, F., Szekely, P., & Zhang, B. (2020). CSKG: The CommonSense Knowledge Graph. arxiv, 2012.11490.
himmelstein2017: Himmelstein, D. S., et al (2017). Systematic integration of biomedical knowledge prioritizes drugs for repurposing. ELife, 6.
xu2019: Xu, L (2019) A Comparison of Learned and Engineered Features in Network-Based Drug Repositioning. Master’s Thesis.
santurkar2018: Santurkar, S., et al. (2018). How does batch normalization help optimization?. Advances in Neural Information Processing Systems.
chao2020: Chao, L., He, J., Wang, T., & Chu, W. (2020). PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.
ding2018: Ding, B., Wang, Q., Wang, B., & Guo, L. (2018). Improving Knowledge Graph Embedding Using Simple Constraints.
balazevic2019b: Balažević, I., Allen, C., & Hospedales, T. (2019). Multi-relational Poincaré Graph Embeddings.