Democratising Knowledge Graphs
Building a knowledge graph for biomedical tasks usually takes months or years. What if you could do it in weeks or days? We created BioCypher to make the process of creating a biomedical knowledge graph easier than ever, but still flexible and transparent. BioCypher is built around the concept of a “threefold modularity”: modularity of data sources, modularity of structure-giving ontology, and modularity of output formats. This design allows for a high degree of flexibility and reusability, rationalising efforts by leveraging the biomedical community.
If you’re new to knowledge graphs and want to familiarise with the concepts that drive BioCypher, we recommend to check out the graphical abstract below and read our paper!
Note
BioCypher is an inclusive community-driven project. If you have any questions, specific needs, or want to contribute to the project, please contact us over on our Zulip channel, on GitHub or via email at sebastian.lobentanzer (at) uni-heidelberg.de.
BioCypher uses a collection of reusable “adapters” for the different sources of biomedical knowledge, which can be flexibly recombined to fit various demands, thus reducing redundant maintenance work through quasi-standardisation. Integrating the controlled vocabularies of ontologies into the process helps to harmonise the data from individual resources and yields a consistent semantic basis for downstream analyses. Through unambiguous and simple “low-code” configuration, a reproducible knowledge graph can be created and shared for every specific task.
Mission Statement
We aim to enable access to versatile and powerful knowledge graphs for as many researchers as possible. Making biomedical knowledge “their own” is often a privilege of the companies and groups that can afford individuals or teams working on knowledge representation in a dedicated manner. With BioCypher, we aim to change that. Creating a knowledge graph should be “as simple as possible, but not any simpler.” To achieve this, we have developed a framework that facilitates the creation of knowledge graphs that are informed by the latest developments in the field of biomedical knowledge representation. However, to make this framework truly accessible and comprehensive, we need the input of the biomedical community. We are therefore inviting you to join us in this endeavour!
Connect your Knowledge Graph to Large Language Models
To facilitate the use of knowledge graphs in downstream tasks, we have developed a framework to connect knowledge graphs to large language models. This framework is called biochatter and is used in our web app ChatGSE. See the links for more information.