See more information on the GitHub Epic.

The goal of Objective C is to provide a clearing house of experiences from within the NFDI in mapping and harmonizing terminologies. We will collect and evaluate mapping experiences, maintain communication with use cases — inviting NFDI participants to exchange their experience and/or surveying them as necessary — and evaluate them in the context of other objectives. The goal is to provide clear examples for others who are attempting mappings and harmonization but also to define improvements that enhance interoperability and reduce mapping costs in the future. In contrast to objective A that considers a more international perspective, this objective focuses more on NFDI wide internal exchange and discussions. The insights and conclusions obtained in Objective C will be shared as part of objective A on a broader international level.

Information about NFDICore ontology

  • Github: NFDICore ontology
  • Documentation: https://ise-fizkarlsruhe.github.io/nfdicore/
  • Discussions related to NFDICore should be done here: https://github.com/ISE-FIZKarlsruhe/nfdicore/discussions

Available SPARQL endpoints

Available Knowledge Graphs

The KGI4NFDI base service has a registry at https://github.com/KGI4NFDI/kgi4nfdi_registry_data. Ideally all KGs used within NFDI should be registered there.

Use Case [1]: Bridging NFDI4Culture / NFDI4Chem Knowledge Graphs

See the blog post by @cthoyt that has been inspired by an invited talk by Torsten Schrade (NFDI4Culture) at the NFDI4Chem consortium’s 6th meeting in Mainz.

What we can do by bridging the 4Chem knowledge graph and 4culture knowledge graph? We can connect datasets in Chemotion electronic laboratory notebooks (ELNs) that annotate the instrument used for generation with depictions of those instruments in cultural heritage objects like paintings.

In sum the data journey described in the blog post does not work, but it depicts where improvement is necessary, which might form the foundation for further joint activities.

Summary of useful links provided in the blog post:

  • Bioregistry is very helpful for resolving needed prefixes
  • PyOBO is a library of reusable tooling to support ingesting new resources across domains in an ontology-like shape. It has been used in the present example to ingest Iconclass data, which appeared to be challenging.
  • Simple Standard for Sharing Ontological Mappings (SSSOM) is a community standard for storing semantic mappings and giving access to standardized tooling for accessing, querying, and applying them.
  • Ubergraph is an RDF triplestore and public SPARQL query endpoint, which includes a novel approach to precomputing OWL inferences. Thus, it can be used for integrating OBO ontologies into a unified semantic graph, see: https://zenodo.org/records/7249759 for more information.