An Open-Source Knowledge Graph Ecosystem for the Life Sciences

TL;DR

PheKnowLator is an open-source ecosystem that enables flexible, customizable, and efficient construction of biomedical knowledge graphs, addressing integration challenges in life sciences research.

Contribution

It introduces a semantic ecosystem with customizable knowledge modeling and comprehensive tools for constructing and analyzing biomedical knowledge graphs.

Findings

Compared to existing methods, PheKnowLator offers greater flexibility in knowledge representation.

The ecosystem demonstrates high computational performance on large-scale KGs.

It facilitates FAIR principles in biomedical data integration.

Abstract

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.