City Research Online

Abstract

Momentum is surging behind the consensus that neural-symbolic AI is the right road for AI to take today. We propose to travel this road using Semantic Web technologies to represent the symbolic AI tradition. Our objective is to investigate and compare the efficacy of a variety of strategies for combining the capabilities of deep neural networks for statistical learning from data with those of OWL ontologies and knowledge graphs for symbolic knowledge representation and reasoning. Our application area is visual relationship detection within images. Deep learning is data hungry and struggles to generalise to examples outside the training distribution. We seek to show that combining Semantic Web domain knowledge and reasoning with deep learning can deliver superior performance, can substitute for plentiful training data, and can deliver robust generalisation in few-shot/zero-shot learning scenarios.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	Copyright © 2022 for the individual papers by the papers' authors. Copyright © 2022 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).
Publisher Keywords:	neural-symbolic, AI, semantic web, knowledge graphs, CEUR-WS
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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