City Research Online

Abstract

Knowledge graphs, as understood within the Semantic Web and Knowledge Representation communities, are more than just graph data. OWL-based knowledge graphs offer the benefits of being based on an ecosystem of open W3C standards that are implemented in a range of reusable existing resources (e.g. curated ontologies, software tools, web-wide linked data) and that also permit researchers to tailor resources for their unique needs (e.g. custom ontologies). Additionally, OWL-based knowledge graphs offer the benefits of formal, logical symbolic reasoning (e.g. reliable inference of new knowledge based on Description Logics, semantic consistency checking, extensions via user-defined Datalog rules). These capabilities allow OWL-based knowledge graphs to be leveraged in the form of active reasoning agents to guide deep learning during training and to participate in refining neural inference. We enumerate a host of such benefits to using OWL-based knowledge graphs in neural-symbolic systems. We illustrate several of these by drawing upon examples from our research in visual relationship detection within images, and we point to promising research directions and challenging opportunities.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Publisher Keywords:	neural-symbolic, AI, deep learning, Semantic Web, OWL, ontologies, knowledge graphs, reasoning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Department of Computer Science

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