SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization

Y Yu, K Huang, C Zhang, LM Glass, J Sun… - …, 2021 - academic.oup.com
Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …

Enhancing taxonomy completion with concept generation via fusing relational representations

Q Zeng, J Lin, W Yu, J Cleland-Huang… - Proceedings of the 27th …, 2021 - dl.acm.org
Automatic construction of a taxonomy supports many applications in e-commerce, web
search, and question answering. Existing taxonomy expansion or completion methods …

Taxocom: Topic taxonomy completion with hierarchical discovery of novel topic clusters

D Lee, J Shen, SK Kang, S Yoon, J Han… - Proceedings of the ACM …, 2022 - dl.acm.org
Topic taxonomies, which represent the latent topic (or category) structure of document
collections, provide valuable knowledge of contents in many applications such as web …

[PDF][PDF] TaxoPrompt: A Prompt-based Generation Method with Taxonomic Context for Self-Supervised Taxonomy Expansion.

H Xu, Y Chen, Z Liu, Y Wen, X Yuan - IJCAI, 2022 - ijcai.org
Taxonomies are hierarchical classifications widely exploited to facilitate downstream natural
language processing tasks. The taxonomy expansion task aims to incorporate emergent …

A review on knowledge graphs for healthcare: Resources, applications, and promises

C Yang, H Cui, J Lu, S Wang, R Xu, W Ma, Y Yu… - arxiv preprint arxiv …, 2023 - arxiv.org
Healthcare knowledge graphs (HKGs) are valuable tools for organizing biomedical concepts
and their relationships with interpretable structures. The recent advent of large language …