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

Authors Jiaoyan Chen , Hang Dong , Janna Hastings , Ernesto Jiménez-Ruiz , Vanessa López , Pierre Monnin , Catia Pesquita , Petr Škoda , Valentina Tamma

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Jiaoyan Chen
  • Department of Computer Science, University of Manchester, UK
  • Department of Computer Science, University of Oxford, UK
Hang Dong
  • Department of Computer Science, University of Oxford, UK
Janna Hastings
  • Institute for Implementation Science in Health Care, University of Zurich, Switzerland
  • School of Medicine, University of St. Gallen, Switzerland
Ernesto Jiménez-Ruiz
  • City, University of London, UK
  • SIRIUS, University of Oslo, Norway
Vanessa López
  • IBM Research Europe, Dublin, Ireland
Pierre Monnin
  • Université Côte d’Azur, Inria, CNRS, I3S, France
Catia Pesquita
  • LASIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
Petr Škoda
  • Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague, Czechia
Valentina Tamma
  • Department of Computer Science, University of Liverpool, UK


We would like to thank Uli Sattler (University of Manchester) for proposing the topic of this paper and Terry Payne (University of Liverpool) for the useful comments on a previous draft. We would also like to thank the TGDK editors in chief for organizing this inaugural issue.

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Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma. Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 5:1-5:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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.

Subject Classification

ACM Subject Classification
  • Information systems → Graph-based database models
  • Computing methodologies → Knowledge representation and reasoning
  • Applied computing → Life and medical sciences
  • Knowledge graphs
  • Life science
  • Knowledge discovery
  • Explainable AI


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