PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings.
Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Sharifzadeh, S., Tresp, V., & Lehmann, J. (2020).
Journal of Machine Learning Research, 22(82), 1–6.

Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework.
Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Galkin, M., Sharifzadeh, S., Fischer, A., Tresp, V., & Lehmann, J. (2020).
arXiv, 2006.13365.

The KEEN Universe.
Ali, M., Jabeen, H., Hoyt, C. T., & Lehmann, J. (2019).
The Semantic Web – ISWC, 2019, 3–18.

Predicting Missing Links Using PyKEEN
Ali, M., Hoyt, C. T., Domingo-Fernández, D., & Lehmann, J. (2019).
In ISWC Satellites, 2019, 245-248.

BioKEEN: a library for learning and evaluating biological knowledge graph embeddings.
Ali, M., Hoyt, C. T., Domingo-Fernández, D., Lehmann, J., & Jabeen, H. (2019).
Bioinformatics, 35(18), 3538–3540.

Metaresearch recommendations using knowledge graph embeddings
Henk, V., Vahdati, S., Nayyeri, M., Ali, M., Yazdi, H. S., & Lehmann, J. (2019).
In RecNLP workshop of AAAI Conference.

Improving Access to Science for Social Good.
Ali, M., Vahdati, S., Singh, S., Dasgupta, S., & Lehmann, J. (2019, September)
In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2019, 658-673.

Selected Citing Papers

CLEP: A Hybrid Data- and Knowledge- Driven Framework for Generating Patient Representations.
Bharadhwaj, V. S., Ali, M., Birkenbihl, C., Mubeen, S., Lehmann, J., Hofmann-Apitius, M., Hoyt, C. T., & Domingo-Fernandez, D. (2020).
bioRxiv, 2020.08.20.259226.

OpenBioLink: A benchmarking framework for large-scale biomedical link prediction
Breit, A., Ott, S., Agibetov, A., & Samwald, M. (2020).
Bioinformatics, btaa274.

Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings
Rivas, A., Grangel-González, I., Collarana, D., Lehmann, J., & Vidal, M. E. (2020).
arXiv, 2006.04556.