Disentangled Ontology Embedding for Zero-Shot Learning

Yuxia Geng, Jiaoyan Chen, Wen Zhang, Yajing Xu, Zhuo Chen, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). However, existing ZSL methods that utilize KGs all neglect the intrinsic complexity of inter-class relationships represented in KGs. One typical feature is that a class is often related to other classes in different semantic aspects. In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects. We also contribute a new ZSL framework named DOZSL, which contains two new ZSL solutions based on generative models and graph propagation models, respectively, for effectively utilizing the disentangled ontology embeddings. Extensive evaluations have been conducted on five benchmarks across zero-shot image classification (ZS-IMGC) and zero-shot KG completion (ZS-KGC). DOZSL often achieves better performance than the state-of-the-art, and its components have been verified by ablation studies and case studies. Our codes and datasets are available at https://github.com/zjukg/DOZSL.
Original languageEnglish
Title of host publicationProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery, Inc
Number of pages11
ISBN (Electronic)9781450393850
Publication statusPublished - 14 Aug 2022
EventThe 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining - Washington D.C., United States
Duration: 14 Aug 202218 Aug 2022
Conference number: 28

Publication series

NameKDD '22
PublisherAssociation for Computing Machinery


ConferenceThe 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Abbreviated titleKDD 2022
Country/TerritoryUnited States
CityWashington D.C.
Internet address


  • disentangled representation learning
  • ontology
  • knowledge graph
  • zero-shot learning


Dive into the research topics of 'Disentangled Ontology Embedding for Zero-Shot Learning'. Together they form a unique fingerprint.

Cite this