Skip to content

Files

Latest commit

ba82bbe · May 19, 2023

History

History
128 lines (89 loc) · 7.46 KB

references.rst

File metadata and controls

128 lines (89 loc) · 7.46 KB

References

.. automodule:: pykeen.models.unimodal
.. automodule:: pykeen.models.multimodal

[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.
[fuhr2018]Fuhr, N. (2018). Some Common Mistakes In IR Evaluation, And How They Can Be Avoided. SIGIR Forum, 51(3), 32–41.
[sakai2021]Sakai, T. (2021). On Fuhr's Guideline for IR Evaluation. SIGIR Forum, 54(1), 1-8.
[galkin2020]Galkin, M., et al. (2020). Message Passing for Hyper-Relational Knowledge Graphs. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 7346–7359.
[wang2019]Wang, X., et al (2019). KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation. arXiv, 1911.06136.
[sun2018]Sun, Z., et al. (2018). Bootstrapping Entity Alignment with Knowledge Graph Embedding. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 4396–4402.
[lin2018]Lin, T.-Y., et al. (2017). Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318–327.
[mukhoti2020]Mukhoti, J., et al. (2020). Calibrating Deep Neural Networks using Focal Loss.
[walsh2020]Walsh, B., et al. (2020). BioKG: A Knowledge Graph for Relational Learning On Biological Data. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 3173–3180.
[nickel2016review]Nickel, M., et al. (2016). A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE, 104(1), 11–33.
[ruffinelli2020]Ruffinelli, D., Broscheit, S., & Gemulla, R. (2020). You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings. International Conference on Learning Representations.
[zhang2017]Zhang, H., et al. (2017). Visual Translation Embedding Network for Visual Relation Detection. arXiv, 1702.08319.
[sharifzadeh2019vrd]Sharifzadeh, S., et al. (2019). Improving Visual Relation Detection using Depth Maps. arXiv, 1905.00966.
[gal2016]Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. ICML 2016.
[zhang2020]Zhang, Y., et al. (2020). AutoSF: Searching Scoring Functions for Knowledge Graph Embedding. ICDE 2020, 433–444.
[tucker1966]Tucker, Ledyard R. (1966). Some mathematical notes on three-mode factor analysis. Psychometrika volume 31, 279–311.
[ali2021]Ali, M., et al (2021). Improving Inductive Link Prediction Using Hyper-relational Facts. ISWC 2021
[teru2020]Teru, K., et al (2020). Inductive Relation Prediction by Subgraph Reasoning. ICML 2020
[zheng2020]Zheng, S., et al (2020). PharmKG: a dedicated knowledge graph benchmark for biomedical data mining. Briefings in Bioinformatics 2020
[berrendorf2020]Berrendorf, M., et al. (2020). On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods.
[yu2021]Yu, L., et al (2021). TripleRE: Knowledge Graph Embeddings via triple Relation Vectors. viXra, 2112.0095.
[hoyt2022]Hoyt, C.T., et al. (2022) A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs. arXiv, 2203.07544.
[chandak2022]Chandak, P., et al (2022). Building a knowledge graph to enable precision medicine. bioRxiv, 2022.05.01.489928.
[wang2022]Wang, L., et al (2022). SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models. arXiv, 2203.02167.
[thanapalasingam2021]Thanapalasingam, T., et al (2021). Relational Graph Convolutional Networks: A Closer Look. arXiv, 2107.10015.
[peng2020]Y. Peng and J. Zhang (2020) LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction, 2020 IEEE International Conference on Data Mining (ICDM), pp. 422-431, doi: 10.1109/ICDM50108.2020.00051.
[koenigs2022]Königs, C., et al (2022) The heterogeneous pharmacological medical biochemical network PharMeBINet, Scientific Data, 9, 393.