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Inductive reasoning in ontologies using conceptual spaces

Bouraoui, Zied, Jameel, Shoaib and Schockaert, Steven 2017. Inductive reasoning in ontologies using conceptual spaces. Presented at: 31st AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 4-9 February 2017.

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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: Conference or Workshop Item (Paper)
Date Type: Completion
Status: In Press
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Related URLs:
Last Modified: 12 Jun 2019 03:04
URI: http://orca.cf.ac.uk/id/eprint/96944

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