City Research Online

Abstract

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 graphstructured.

The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledgedriven 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.

Publication Type:	Article
Additional Information:	© Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma; licensed under Creative Commons License CC-BY 4.0
Publisher Keywords:	Knowledge graphs; Life science; Knowledge discovery; Explainable AI
Subjects:	H Social Sciences > HM Sociology Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine
Departments:	School of Science & Technology > Computer Science

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