Skip to main content

Inductive Reasoning about Ontologies Using Conceptual Spaces

Bouraoui, Zied, Shoaib, Jameel, Schockaert, Steven (2017) Inductive Reasoning about Ontologies Using Conceptual Spaces. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, . ISSN 2159-5399. E-ISSN 2374-3468. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

PDF - Publisher pdf
Restricted to Repository staff only
Contact us about this Publication Download (283kB)
[img]
Official URL
https://www.aaai.org/ocs/index.php/AAAI/AAAI17/pap...

Abstract

Structured knowledge about concepts plays an increasingly important role in areas such as information retrieval. The available ontologies and knowledge graphs that encode such conceptual knowledge, however, are inevitably incomplete. This observation has led to a number of methods that aim to automatically complete existing knowledge bases. Unfortunately, most existing approaches rely on black box models, e.g. formulated as global optimization problems, which makes it difficult to support the underlying reasoning process with intuitive explanations. In this paper, we propose a new method for knowledge base completion, which uses interpretable conceptual space representations and an explicit model for inductive inference that is closer to human forms of commonsense reasoning. Moreover, by separating the task of representation learning from inductive reasoning, our method is easier to apply in a wider variety of contexts. Finally, unlike optimization based approaches, our method can naturally be applied in settings where various logical constraints between the extensions of concepts need to be taken into account.

Item Type: Article
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Shoaib Jameel
Date Deposited: 16 Oct 2018 14:26 UTC
Last Modified: 14 Jan 2020 15:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69600 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year