Engineering & Manufacturing

At Siemens, the Ephedra engine enables the transparent integration of the intenal knowledge graph with the dynamic data produced by the API services available in the infrastructure, and with analysis results produced by machine learning algorithms. By supporting hybrid data integration and querying, metaphactory allows for building several distinct knowledge graph applications targeting different user groups, and supports several practical end-user scenarios within the Siemens infrastructure. Examples include gas turbine diagnostics and maintenance, and building automation and Digital Twin creation.

Pharma & Life Sciences

In the pharmaceutics domain, metaphactory and the Ephedra engine allow domain experts to augment internal knowledge graphs containing, e.g., interlinked data about genes, proteins, and associated diseases, with additional relevant private or public data sources (e.g., Wikidata and Nextprot) and domain-specific services (e.g., KNIME or BLAST). They can then use full-text indices to perform custom keyword search in addition to the SPARQL structured search.

Open Data

The applications below showcase the power of the metaphactory platform and the Ephedra federation engine in the open data domain:

• A map combining data about the population density in particular regions and the coordinates of these regions » Go to app
• A map combining data about the entities located within a particular administrative entity and the coordinates of these entities » Go to app
• An application that allows users to search for entities similar to a given entity, where the similarity is provided by an ML-based model » Go to app