×

Learning distributed representations of high-arity relational data with non-linear relational embedding. (English) Zbl 1037.68703

Kaynak, Okyay (ed.) et al., Artificial neural networks and neural information processing — ICANN/ICONIP 2003. Joint international conference ICANN/ICONIP 2003, Istanbul, Turkey, 26–29, 2003. Proceedings. Berlin: Springer (ISBN 3-540-40408-2/pbk). Lect. Notes Comput. Sci. 2714, 149-156 (2003).
Summary: 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.
For the entire collection see [Zbl 1029.00055].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
92B20 Neural networks for/in biological studies, artificial life and related topics
PDFBibTeX XMLCite
Full Text: Link