Computer Science > Artificial Intelligence
[Submitted on 29 Sep 2023 (v1), last revised 20 Dec 2023 (this version, v4)]
Title:Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
View PDF HTML (experimental)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 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.
Submission history
From: Hang Dong [view email][v1] Fri, 29 Sep 2023 14:03:34 UTC (736 KB)
[v2] Fri, 6 Oct 2023 15:02:55 UTC (736 KB)
[v3] Mon, 27 Nov 2023 08:57:10 UTC (769 KB)
[v4] Wed, 20 Dec 2023 13:34:31 UTC (771 KB)
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