Projects per year
Abstract / Description of output
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.
Original language | English |
---|---|
Publisher | ArXiv |
DOIs | |
Publication status | Published - 31 May 2023 |
Keywords / Materials (for Non-textual outputs)
- knowledge graphs
- knowledge graph embeddings
- polypharmacy
- rule-based learning
- transfer learning
Fingerprint
Dive into the research topics of 'Knowledge graph embeddings in the biomedical domain: Are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks'. Together they form a unique fingerprint.Projects
- 2 Active
-
TEAMER : Teaching Machines to Reason Like Humans
Engineering and Physical Sciences Research Council
1/10/21 → 30/09/26
Project: Research
-
KATY: Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity
1/01/21 → 30/06/25
Project: Research