The following are some of my published works and articles.
You can also find my profile on Google Scholar and Semantic Scholar.
Publications
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Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks
2023
(Preprint, to be reviewed)
Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent study demonstrates the limited efficacy of these embedding algorithms when applied to biomedical knowledge graphs, raising the question of whether knowledge graph embeddings have limitations in biomedical settings. This study aims to apply state-of-the-art knowledge graph embedding models in the context of a recent biomedical knowledge graph, BioKG, and evaluate their performance and potential downstream uses. We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph. Additionally, we provide interpretable predictions through a rule-based method. We demonstrate that knowledge graph embedding models are applicable in practice by evaluating the best-performing model on four tasks that represent real-life polypharmacy situations. Results suggest that knowledge learnt from large biomedical knowledge graphs can be transferred to such downstream use cases.
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Producing Creative Chess through Chess Engine Selfplay
Wolf De Wulf
In Proceedings of the 12th International Conference on Computational Creativity, 2021
This article presents preliminary work on a creative chess engine that can be used to produce creative chess games or sequences. The contribution in this article is the creation of a creative chess engine that is then pitted against itself to form a creative system that outputs chess games. The chess engine is an extension to an existing chess engine that consists of forcing the existing engine to play more creative moves. It is in no way an improvement when compared to existing chess engines, even though it is based on the world’s best: Stockfish. Letting the supposedly creative chess engine play against itself forms a creative system that outputs chess games. Through analyzing these games it might be possible to discover new chess openings or principles.
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QMaxSATpb: A Certified MaxSAT Solver
In Logic Programming and Nonmonotonic Reasoning, 2022
While certification has been successful in the context of satisfiablity solving, with most state-of-the-art solvers now able to provide proofs of unsatisfiability, in maximum satisfiability, such techniques are not yet widespread. In this paper, we present QMaxSATpb, an extension of QMaxSAT that can produce proofs of optimality in the VeriPB proof format, which itself builds on the well-known cutting planes proof system. Our experiments demonstrate that proof logging is possible without much overhead.
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LP2PB: Translating Answer Set Programs into Pseudo-Boolean Theories
In Proceedings of the 36th International Conference on Logic Programming: Technical Communications, 2020
Answer set programming (ASP) is a well-established knowledge representation formalism. Most ASP solvers are based on (extensions of) technology from Boolean satisfiability solving. While these solvers have shown to be very successful in many practical applications, their strength is limited by their underlying proof system, resolution. In this paper, we present a new tool LP2PB that translates ASP programs into pseudo-Boolean theories, for which solvers based on the (stronger) cutting plane proof system exist. We evaluate our tool, and the potential of cutting-plane–based solving for ASP on traditional ASP benchmarks as well as benchmarks from pseudo-Boolean solving. Our results are mixed: overall, traditional ASP solvers still outperform our translational approach, but several benchmark families are identified where the balance shifts the other way, thereby suggesting that further investigation into a stronger proof system for ASP is valuable.
Theses
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Transfer learning in Brain-Computer Interfaces: Language-Pretrained Transformers for Classifying Electroencephalography
2022
(MSc Computer Science, Vrije Universiteit Brussel)