Mehdi Ali
University of Bonn
19 Papers
65 Citations
Mehdi Ali is an academic researcher from University of Bonn. The author has contributed to research in topics: Computer science & Python (programming language). The author has an hindex of 7, co-authored 19 publications. Previous affiliations of Mehdi Ali include Fraunhofer Society.
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Papers
•Posted Content
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Mehdi Ali,Max Berrendorf,Charles Tapley Hoyt,Laurent Vermue,Sahand Sharifzadeh,Volker Tresp,Jens Lehmann +6 more
TL;DR: PyKEEN 1.0 is re-designed and re-implemented, one of the first KGE libraries, in a community effort, and through the integration of Optuna extensive hyper-parameter optimization (HPO) functionalities are provided.
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BioKEEN: a library for learning and evaluating biological knowledge graph embeddings.
Mehdi Ali,Charles Tapley Hoyt,Charles Tapley Hoyt,Daniel Domingo-Fernández,Daniel Domingo-Fernández,Jens Lehmann,Jens Lehmann,Hajira Jabeen +7 more
TL;DR: This work developed BioKEEN and PyKEEN to facilitate their easy use through an interactive command line interface and presents a case study in which a novel biological pathway mapping resource is used to predict links that represent pathway crosstalks and hierarchies.
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Improving Inductive Link Prediction Using Hyper-relational Facts
Mehdi Ali,Mehdi Ali,Max Berrendorf,Mikhail Galkin,Veronika Thost,Tengfei Ma,Volker Tresp,Jens Lehmann,Jens Lehmann +8 more
- 24 Oct 2021
TL;DR: In this paper, the authors classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
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BioKEEN: A library for learning and evaluating biological knowledge graph embeddings
Mehdi Ali,Charles Tapley Hoyt,Charles Tapley Hoyt,Daniel Domingo-Fernández,Daniel Domingo-Fernández,Jens Lehmann,Jens Lehmann,Hajira Jabeen +7 more
TL;DR: This work developed BioKEEN and PyKEEN to facilitate their easy use through an interactive command line interface and presents a case study in which a novel biological pathway mapping resource is used to predict links that represent pathway crosstalks and hierarchies.
The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.
Sumit Madan,Justyna Szostak,Ravikumar Komandur Elayavilli,Richard Tzong-Han Tsai,Mehdi Ali,Longhua Qian,Majid Rastegar-Mojarad,Julia Hoeng,Juliane Fluck +8 more
TL;DR: Within the BEL track, training data and an evaluation environment are provided to encourage the text mining community to tackle the automatic extraction of complex BEL relationships and to support the tedious and time-intensive extraction work of curators with automated methods.
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