Unit mission

SIB’s Knowledge Representation Unit interoperates and integrates diverse data stored in multiple locations and formats, and develops pioneering interfaces to quickly access and explore interconnected databases. 

This increases the value of life science data by:

  • maximizing data reuse by people and AI systems
  • connecting knowledge within and across fields
  • enabling a holistic view of complex biological systems
  • generating more powerful insights than possible from individual databases.

The unit is co-led by Tarcisio Mendes de Farias and Ana-Claudia Sima and is part of the SIB Vital-IT Computational Biology group at the SIB Hub. Its members contribute to a wide variety of Swiss and international research projects involving academic, hospital, and industry partners, and collaborate across the SIB network to enhance bioinformatics tools, resources and discovery

Explore SIB’s knowledge representation services

Knowledge representation expertise

The team has extensive expertise in semantic web technologies for formally describing, structuring and connecting data in a meaningful way, as well as in building user-friendly tools for efficient retrieval of data and information across multiple databases.

  • Ontology engineering: developing machine-readable ontologies (or vocabularies) and metadata standards for describing biodata and their relationships, and applying these to heterogenous databases from different sources, fields and/or locations.
  • Knowledge graphs: structuring metadata and databases into integrated networks of linked data that seamlessly interconnect information stored in the different databases.
  • Database querying: using SPARQL and other technical query languages for retrieving and compiling relevant information from large quantities of complex data from different sources.
  • Semantic search and natural language processing: delivering user-friendly interfaces for fast and easy data access, retrieval, and compilation, by integrating knowledge representation with sophisticated keyword search and Large Language Model (LLM)-based generative AI technologies.

Advancing knowledge discovery and preservation

The SIB Knowledge Representation Unit contributes to a variety of multi-site, cutting-edge data projects in Switzerland and internationally – many of which are funded by highly competitive grants, including from the Swiss National Science Foundation (SNSF), swissuniversities, The Loop Zurich, and Horizon Europe.

  • Advancing the European Open Science Cloud (EOSC): The multi-partner EOSC EDEN, FIDELIS, and EOSC Data Commons projects will ensure the long-term preservation of Europe’s data assets and allow AI-based discovery of data and tools across the entire collection of European research outputs. FIDELIS also involves the SIB Biodata Resources and Training teams. 

  • Enabling AI-driven precision oncology: The AI Tumor Board project with Swiss hospitals is converting unstructured PDF guidelines for cancer care into structured, interoperable treatment procedures, and building an AI tool to analyse the procedures plus historical clinical data to predict the best treatment for individual patients.

Members

View Knowledge Representation Unit members here

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