Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
This work addresses the problem of integrating dynamic biomedical knowledge graphs into AI applications for researchers, but it is incremental as it extends an existing API to a specific domain.
The authors tackled the need for up-to-date knowledge graph embeddings in biomedical research by developing Bio-KGvec2go, an extension of the KGvec2go Web API that generates and serves embeddings for widely used biomedical ontologies with regular updates aligned with ontology releases, facilitating efficient and timely research.
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating these semantic resources with machine learning models often relies on knowledge graph embedding models to transform graph data into numerical representations. Therefore, pre-trained models for popular knowledge graphs and ontologies are increasingly valuable, as they spare the need to retrain models for different tasks using the same data, thereby helping to democratize AI development and enabling sustainable computing. In this paper, we present Bio-KGvec2go, an extension of the KGvec2go Web API, designed to generate and serve knowledge graph embeddings for widely used biomedical ontologies. Given the dynamic nature of these ontologies, Bio-KGvec2go also supports regular updates aligned with ontology version releases. By offering up-to-date embeddings with minimal computational effort required from users, Bio-KGvec2go facilitates efficient and timely biomedical research.