Knowledge engineering

To process data efficiently, a strategy regarding knowledge building and handling is needed. Each project needs and creates knowledge. Therefore we need to store and access this new knowledge as well as linking it in a coherent network of meaning. This strategy revolves around three axes.

Knowledge management & ontologies for phenotypes

Building knowledge in a meaningful way and save it in a manner that allow easy access for new projects. How to add semantic to multiple type of data and handle its evolution when new discoveries come.

    • Semantic amplification
    • Semantic centric data representation

Conceptual representation of knowledge

Conceptual representation of knowledge cannot be limited to reference textbooks and dictionaries. It is therefore crucial to explore new possibilities to represent and efficiently use this knowledge. Formal languages, graph databases or ontology engines are some examples of those approaches.

    • Storage – triple store
    • Generic descriptive
    • RDF Query - SPARQL

Lexico-semantic resources

Lexico-sementic resources are the fuel of the knowledge engineering domain. They contain the semantic and allow the coherent linking of data generated in the healthcare setting. Building and maintaining of those resources are a major role of this domain.

    • Manually annotated and classified clinical documents
    • Large manually multicoded expression corpuses
    • Clinical questionnaires manually SNOMED-CT encoded language specific: French, German, Italian, English, Greek