Biomedical association inference on pandemic knowledge graphs A comparative study

Wu, M; Yu, C; Xu, J; Ding, Y; Zhang, Y

Biomedical association inference on pandemic knowledge graphs A comparative study

Wu, M

Yu, C Xu, J Ding, Y Zhang, Y

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Publisher:: CEUR-WS
Publication Type:: Conference Proceeding
Citation:: CEUR Workshop Proceedings, 2024, 3745, pp. 124-127
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Wu, M https://orcid.org/0000-0003-3956-7808
dc.contributor.author	Yu, C
dc.contributor.author	Xu, J
dc.contributor.author	Ding, Y
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.date	2024-04-23
dc.date.accessioned	2025-03-09T21:09:11Z
dc.date.available	2025-03-09T21:09:11Z
dc.date.issued	2024-01-01
dc.identifier.citation	CEUR Workshop Proceedings, 2024, 3745, pp. 124-127
dc.identifier.issn	1613-0073
dc.identifier.uri	http://hdl.handle.net/10453/185598
dc.description.abstract	Acquiring insights and understanding from historical pandemics is crucial for reducing the likelihood of their recurrence. The utilization of knowledge graphs stands as an essential tool for researchers, with knowledge inference emerging as a prominent task within these graphs to deduce previously unidentified connections between entities. This study endeavors to construct a knowledge graph centered on pandemic research and to evaluate the efficacy of various mainstream methodologies in the context of biomedical association inference. Our findings indicate that techniques for graph representation hold significant promise in executing these tasks and heterogeneous graph representation techniques demonstrate high predicting accuracy. Nonetheless, the advancement in this area of research necessitates more refined experimental designs and the adoption of more adaptive learning strategies.
dc.language	en
dc.publisher	CEUR-WS
dc.relation.ispartof	CEUR Workshop Proceedings
dc.relation.ispartof	Joint Workshop of the Extraction and Evaluation of Knowledge Entities from Scientific Documents and the AI + Informetrics
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4609 Information systems
dc.title	Biomedical association inference on pandemic knowledge graphs A comparative study
dc.type	Conference Proceeding
utslib.citation.volume	3745
utslib.location.activity	Changchun, China
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Stroke Research Collaborative
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-03-09T21:09:09Z
pubs.finish-date	2024-04-24
pubs.publication-status	Published
pubs.start-date	2024-04-23
pubs.volume	3745

Abstract:

Acquiring insights and understanding from historical pandemics is crucial for reducing the likelihood of their recurrence. The utilization of knowledge graphs stands as an essential tool for researchers, with knowledge inference emerging as a prominent task within these graphs to deduce previously unidentified connections between entities. This study endeavors to construct a knowledge graph centered on pandemic research and to evaluate the efficacy of various mainstream methodologies in the context of biomedical association inference. Our findings indicate that techniques for graph representation hold significant promise in executing these tasks and heterogeneous graph representation techniques demonstrate high predicting accuracy. Nonetheless, the advancement in this area of research necessitates more refined experimental designs and the adoption of more adaptive learning strategies.

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http://hdl.handle.net/10453/185598