Learning distributed representations of high-arity relational data with non-linear relational embedding

Authors:
Alberto Paccanaro
Affiliations:
Dept. of Medical Microbiology, Queen Mary University of London, UK
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 3
Cited 1

Recursive distributed representations

Artificial Intelligence - On connectionist symbol processing
Learning Distributed Representations of Concepts Using Linear Relational Embedding

IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations

Machine Learning

Representing objects, relations, and sequences

Neural Computation

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Abstract

We summarize Linear Relational Embedding (LRE), a method which has been recently proposed for generalizing over relational data. We show that LRE can represent any binary relations, but that there are relations of arity greater than 2 that it cannot represent. We then introduce Non-Linear Relational Embedding (NLRE) and show that it can learn any relation. Results of NLRE on the Family Tree Problem show that generalization is much better than the one obtained using backpropagation on the same problem.