mlsanityData to 14 Oct 2026

Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

arXiv 2309.17255 ↗First posted 29 Sep 2023cs.AI cs.CLcited by 10

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Abstract (arXiv)

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 55 of its 212 references that are in the map. First posted on arXiv 29 Sep 2023.

Builds on

55 of 212 references.

References · 55

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38 more
  1. 2021Graph Representation Learning in Biomedicinecited by 80
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 1; 2026: 3.

10 papers.

  1. 2023RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA moleculescited by 5
  2. 2024Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Datacited by 4
  3. 2024A Language Model based Framework for New Concept Placement in Ontologiescited by 3
  4. 2026The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labscited by 3
  5. 2025Exploring a Large Language Model for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Developmentcited by 1
  6. 2026Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddingscited by 1
  7. 2024Evaluating Class Membership Relations in Knowledge Graphs using Large Language Modelscited by 0
  8. 2024Repairing Networks of EL_perp Ontologies using Weakening and Completing -- Extended versioncited by 0
  9. 2024KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruningcited by 0
  10. 2026Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networkscited by 0