Abstract
Lifted relational neural networks (LRNNs) are a flexible neural-symbolic framework based on the idea of lifted modelling. In this paper we show how LRNNs can be easily used to specify declaratively and solve learning problems in which latent categories of entities, properties and relations need to be jointly induced.
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CITATION STYLE
Šourek, G., Manandhar, S., Železný, F., Schockaert, S., & Kuželka, O. (2017). Learning predictive categories using lifted relational neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10326 LNAI, pp. 108–119). Springer Verlag. https://doi.org/10.1007/978-3-319-63342-8_9
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