  Translational research requires data at multiple scales of biological
organization. Advancements in sequencing and multi-omics technologies have
increased the availability of these data, but researchers face significant
integration challenges. Knowledge graphs (KGs) are used to model complex
phenomena, and methods exist to construct them automatically. However, tackling
complex biomedical integration problems requires flexibility in the way
knowledge is modeled. Moreover, existing KG construction methods provide robust
tooling at the cost of fixed or limited choices among knowledge representation
models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem
for automating the FAIR (Findable, Accessible, Interoperable, and Reusable)
construction of ontologically grounded KGs with fully customizable knowledge
representation. The ecosystem includes KG construction resources (e.g., data
preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction
algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated
the ecosystem by systematically comparing it to existing open-source KG
construction methods and by analyzing its computational performance when used
to construct 12 large-scale KGs. With flexible knowledge representation,
PheKnowLator enables fully customizable KGs without compromising performance or
usability.
