Community knowledge graph abstraction for enhanced link prediction: A study on PubMed knowledge graph
This study proposed novel insights into abstracting CKGs from the PKG. The extension approach indicated enhanced performance of the existing KGE methods and has applicability. As an interesting future extension, we plan to conduct link prediction for entities that are newly introduced to the PKG.
Authors
- Tomotake Kozu (ORCID: https://orcid.org/0009-0005-9864-7383)
- Shunsuke Hirose (ORCID: https://orcid.org/0000-0002-2825-2790)
- Yang Zhao
- Danushka Bollegala (ORCID: https://orcid.org/0000-0003-4476-7003)
- Yingzi Jin (ORCID: https://orcid.org/0009-0009-0813-4859)
Institutions
- Deloitte (United States) (US)
- University of Liverpool (GB)
- Deloitte (United Kingdom) (GB)
- Deloitte (Czechia) (CZ)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2024-09-10
- DOI
- https://doi.org/10.1016/j.jbi.2024.104725
- Citations
- 3
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.70