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.

翻译：生命科学一词泛指研究生物体及生命过程的学科，涵盖化学、生物学、医学及其他相关领域。该领域的研究工作高度依赖数据驱动，不仅产生海量科学数据，更需处理这些数据——其中大部分具有内在关联性和图结构特征。数据规模与科学概念及其关系的复杂性，促使先进知识驱动技术被广泛应用于数据管理与解析，最终目标在于推动科学发现进程。作为一篇综述与立场声明论文，我们探讨了基于图的技术在生命科学领域的最新发展进展，并展望这些技术对未来该领域的影响前景。本文聚焦三大主题：知识图谱的构建与管理、利用知识图谱及相关技术发现新知识，以及知识图谱在人工智能应用中支持可解释性。针对每个主题，我们选取若干典型案例，深入探讨其中挑战与开放性问题，最后通过总结全局性挑战及其潜在解决方案的视角与展望，为未来研究提供指南。