Ontology-guided Semantic Composition for Zero-Shot Learning

Jiaoyan Chen, Freddy Lecue, Yuxia Geng, Jeff Z. Pan, Huajun Chen

Research output: Working paper


Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, we propose to model the compositional and expressive semantics of class labels by an OWL (Web Ontology Language) ontology, and further develop a new ZSL framework with ontology embedding. The effectiveness has been verified by some primary experiments on ani- mal image classification and visual question answering.
Original languageEnglish
Number of pages5
Publication statusSubmitted - 30 Jun 2020

Bibliographical note

Accepted by KR 2020 - 17th International Conference on Principles of Knowledge Representation and Reasoning.

We want to thank Ian Horrocks from University of Ox-ford for helpful discussions. The work is supported by the AIDA project (Alan Turing Institute), the SIRIUS Centre for Scalable Data Access (Research Council of Norway), Samsung Research UK, Siemens AG, and the EPSRC projects AnaLOG (EP/P025943/1), OASIS (EP/S032347/1) and UKFIRES (EP/S019111/1)


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