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.
Description
[2309.17255] Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
%0 Generic
%1 tgdk2023lifeSciKnowledge
%A Chen, Jiaoyan
%A Dong, Hang
%A Hastings, Janna
%A Jiménez-Ruiz, Ernesto
%A Lopez, Vanessa
%A Monnin, Pierre
%A Pesquita, Catia
%A Škoda, Petr
%A Tamma, Valentina
%D 2023
%K chebi chemical explainable-ai gene-ontology go kg kg-construction kgs knowledge-discovery knowledge-graph-construction knowledge-graphs life-science-knowledge-discovery life-sciences llm medical multimodal myown ontologies personal_knowledge_graph personalisation protein snomedct xai
%T Knowledge Graphs for the Life Sciences: Recent Developments, Challenges
and Opportunities
%U http://arxiv.org/abs/2309.17255
%X 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.
@misc{tgdk2023lifeSciKnowledge,
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.},
added-at = {2023-10-02T10:18:24.000+0200},
author = {Chen, Jiaoyan and Dong, Hang and Hastings, Janna and Jiménez-Ruiz, Ernesto and Lopez, Vanessa and Monnin, Pierre and Pesquita, Catia and Škoda, Petr and Tamma, Valentina},
biburl = {https://www.bibsonomy.org/bibtex/21836c8ffd51e1483daf0f14ef0795f76/hangdong},
description = {[2309.17255] Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities},
interhash = {821dfc2307fd60bd37ac4dccc90a7ffb},
intrahash = {1836c8ffd51e1483daf0f14ef0795f76},
keywords = {chebi chemical explainable-ai gene-ontology go kg kg-construction kgs knowledge-discovery knowledge-graph-construction knowledge-graphs life-science-knowledge-discovery life-sciences llm medical multimodal myown ontologies personal_knowledge_graph personalisation protein snomedct xai},
note = {cite arxiv:2309.17255Comment: 32 pages, 1 figure, accepted for Transactions on Graph Data and Knowledge (TGDK)},
timestamp = {2023-10-02T10:18:24.000+0200},
title = {Knowledge Graphs for the Life Sciences: Recent Developments, Challenges
and Opportunities},
url = {http://arxiv.org/abs/2309.17255},
year = 2023
}