| PyKEEN 1.0: a python library for training and evaluating knowledge graph embeddings M Ali, M Berrendorf, CT Hoyt, L Vermue, S Sharifzadeh, V Tresp, ... Journal of Machine Learning Research 22 (82), 1-6, 2021 | 183 | 2021 |
| Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework M Ali, M Berrendorf, CT Hoyt, L Vermue, M Galkin, S Sharifzadeh, ... IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109 …, 2021 | 135 | 2021 |
| BioKEEN: a library for learning and evaluating biological knowledge graph embeddings M Ali, CT Hoyt, D Domingo-Fernández, J Lehmann, H Jabeen Bioinformatics 35 (18), 3538-3540, 2019 | 33 | 2019 |
| Improving inductive link prediction using hyper-relational facts M Ali, M Berrendorf, M Galkin, V Thost, T Ma, V Tresp, J Lehmann The Semantic Web–ISWC 2021: 20th International Semantic Web Conference, ISWC …, 2021 | 25 | 2021 |
| Metaresearch recommendations using knowledge graph embeddings V Henk, S Vahdati, M Nayyeri, M Ali, HS Yazdi, J Lehmann The AAAI-19 Workshop on Recommender Systems and Natural Language Processing …, 2019 | 19 | 2019 |
| The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track S Madan, J Szostak, R Komandur Elayavilli, RTH Tsai, M Ali, L Qian, ... Database 2019, baz084, 2019 | 19 | 2019 |
| The KEEN universe: An ecosystem for knowledge graph embeddings with a focus on reproducibility and transferability M Ali, H Jabeen, CT Hoyt, J Lehmann The Semantic Web–ISWC 2019: 18th International Semantic Web Conference …, 2019 | 18 | 2019 |
| CLEP: a hybrid data-and knowledge-driven framework for generating patient representations VS Bharadhwaj, M Ali, C Birkenbihl, S Mubeen, J Lehmann, ... Bioinformatics 37 (19), 3311-3318, 2021 | 11 | 2021 |
| Integration of structured biological data sources using biological expression language CT Hoyt, D Domingo-Fernández, S Mubeen, JM Llaó, A Konotopez, ... Biorxiv, 631812, 2019 | 9 | 2019 |
| Affinity Dependent Negative Sampling for Knowledge Graph Embeddings. MM Alam, H Jabeen, M Ali, K Mohiuddin, J Lehmann DL4KG@ ESWC, 2020 | 7 | 2020 |
| Automatic extraction of BEL-statements based on neural networks M Ali, S Madan, A Fischer, H Petzka, J Fluck Proceedings of the sixth BioCreative challenge evaluation workshop …, 2017 | 6 | 2017 |
| Tokenizer Choice For LLM Training: Negligible or Crucial? M Ali, M Fromm, K Thellmann, R Rutmann, M Lübbering, J Leveling, ... | 5 | 2023 |
| Improving access to science for social good M Ali, S Vahdati, S Singh, S Dasgupta, J Lehmann Machine Learning and Knowledge Discovery in Databases: International …, 2020 | 4 | 2020 |
| Predicting Missing Links Using PyKEEN M Ali, CT Hoyt, D Domingo-Fernández, J Lehmann | 4 | 2019 |
| Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions? AA Weber, K Thellmann, J Ebert, N Flores-Herr, J Lehmann, M Fromm, ... arXiv preprint arXiv:2402.13703, 2024 | | 2024 |
| Investigating Graph Representation Learning Methods For Link Prediction in Knowledge Graphs M Ali University of Bonn, Germany, 2023 | | 2023 |
| Metadata standards for the FAIR sharing of vector embeddings in Biomedicine S Kafkas, R Celebi, M Ali, H Jabeen, M Dumontier, R Hoehndorf 28th Conference on Intelligent Systems for Molecular Biology: Bio-Ontologies …, 2020 | | 2020 |
