We build on recent technological and conceptual developments in biomedical ontologies that greatly facilitate the harmonisation of biomedical knowledge and advocate a philosophy of reuse of open-source software. For instance, we integrate a comprehensive “high-level” biomedical ontology, the Biolink model [@doi:10.1111/cts.13302], which can be replaced or extended by more domain-specific ontologies as needed, and an extensive catalogue and resolver for biomedical identifier resources, the Bioregistry [@doi:10.1038/s41597-022-01807-3]. Both projects, like BioCypher, are open-source and community-driven. The ontologies serve as a framework for the representation of biomedical concepts; by supporting the Web Ontology Language (OWL), BioCypher allows integration and manipulation of most ontologies, including those generated by Large Language Models.
Separating the ontology framework from the modelled data allows implementation of reasoning applications at the ontology level, for instance the ad-hoc harmonisation of multiple disease ontologies before mapping the data points. For instance, with a group of users that are knowledgeable in ontology, a way to harmonise the divergent or incomplete ontologies can be developed, e.g. on the topic of diseases, before using them to inform the knowledge representation output. In addition, new developments in the field of language models and grounding will enable plugging “automatic” grounding into the ontology adapter in BioCypher, helping more novice users with the mapping between KG entities and the corresponding ontologies (see for instance https://github.com/ccb-hms/ontology-mapper).
Building a task-specific KG, given existing configuration, takes only minutes, and creating a KG from scratch can be achieved in a few days of work. This allows for rapid prototyping and automated machine learning (ML) pipelines that iterate the KG structure to optimise predictive performance; for instance, building custom task-specific KGs for graph embeddings and ML (see case study “Embeddings”). Despite its speed, automated testing of millions of entities and relationships per KG increases trust in the consistency of the data (see Supplementary Methods for details and the case study “Network expansion” for an example).