Development of graph-based knowledge structures that represent entities, relationships, and connections across heterogeneous datasets.
Projects can involve ontologies, entity resolution, graph construction, linked data, semantic relationships, and graph-based querying.
Knowledge graphs are useful in scientific knowledge, biodiversity, drug discovery, enterprise information, and research databases.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.