# An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv: https://arxiv.org/abs/2307.05727 · HTML: https://mlsanity.com/paper/2307.05727/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 119 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [OpenBioLink: A benchmarking framework for large-scale biomedical link prediction](https://mlsanity.com/paper/1912.04616/) (2019)
- [Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science](https://mlsanity.com/paper/2203.13906/) (2022)
- [Knowledge-based Biomedical Data Science 2019](https://mlsanity.com/paper/1910.06710/) (2019)
- [GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding](https://mlsanity.com/paper/2110.06196/) (2021)
- [KG-Hub -- Building and Exchanging Biological Knowledge Graphs](https://mlsanity.com/paper/2302.10800/) (2023)
- [Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding](https://mlsanity.com/paper/2101.01425/) (2021)
- [Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions](https://mlsanity.com/paper/2209.11950/) (2022)
- [Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome](https://mlsanity.com/paper/2207.14294/) (2022)

## Built on by (most cited first, 11 of 11)

- [RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules](https://mlsanity.com/paper/2312.00183/) (2023)
- [A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology](https://mlsanity.com/paper/2408.03866/) (2024)
- [An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News](https://mlsanity.com/paper/2509.02258/) (2025)
- [LINK-KG: LLM-Driven Coreference-Resolved Knowledge Graphs for Human Smuggling Networks](https://mlsanity.com/paper/2510.26486/) (2025)
- [Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs](https://mlsanity.com/paper/2510.26512/) (2025)
- [A review of feature selection strategies utilizing graph data structures and knowledge graphs](https://mlsanity.com/paper/2406.14864/) (2024)
- [CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks](https://mlsanity.com/paper/2506.21607/) (2025)
- [VitaGraph: Building a Knowledge Graph for Biologically Relevant Learning Tasks](https://mlsanity.com/paper/2505.11185/) (2025)
- [A Vision for Multisensory Intelligence: Sensing, Science, and Synergy](https://mlsanity.com/paper/2601.04563/) (2026)
- [Plausibility-Driven Prioritization of Candidate Biomedical Annotations](https://mlsanity.com/paper/2607.20163/) (2026)
- [Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2609.18481/) (2026)

## Abstract (arXiv)

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.