Mehdi Ali
Mehdi Ali
Fraunhofer IAIS, LAMARR Institute
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
Integration of structured biological data sources using biological expression language
CT Hoyt, D Domingo-Fernández, S Mubeen, JM Llaó, A Konotopez, ...
Biorxiv, 631812, 2019
Affinity Dependent Negative Sampling for Knowledge Graph Embeddings.
MM Alam, H Jabeen, M Ali, K Mohiuddin, J Lehmann
DL4KG@ ESWC, 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
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
Predicting Missing Links Using PyKEEN
M Ali, CT Hoyt, D Domingo-Fernández, J Lehmann
Tokenizer Choice For LLM Training: Negligible or Crucial?
M Ali, M Fromm, K Thellmann, R Rutmann, M Lübbering, J Leveling, ...
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
Investigating Graph Representation Learning Methods For Link Prediction in Knowledge Graphs
M Ali
Universitäts-und Landesbibliothek Bonn, 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
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