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2017 | OriginalPaper | Chapter

Learning Predictive Categories Using Lifted Relational Neural Networks

Authors : Gustav Šourek, Suresh Manandhar, Filip Železný, Steven Schockaert, Ondřej Kuželka

Published in: Inductive Logic Programming

Publisher: Springer International Publishing

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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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previous chapter Towards Nonmonotonic Relational Learning from Knowledge Graphs

next chapter Generation of Near-Optimal Solutions Using ILP-Guided Sampling

Established notions such as “rule” are further used also for their weighted analogies.

These represent aggregation operators that can take a variable number of arguments.

In general LRNNs support non-ground query atoms but in this paper we will not need them. Therefore we assume only ground query atoms for simplicity.

The membership degrees are simply obtained as applying sigmoids on the respective weights in this particular case, so the two representations essentially bear the same information.

Downloaded from https://alchemy.cs.washington.edu/data/animals/.

Downloaded from https://alchemy.cs.washington.edu/data/nations/ and from https://alchemy.cs.washington.edu/data/umls/.

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Title: Learning Predictive Categories Using Lifted Relational Neural Networks
Authors: Gustav Šourek
Suresh Manandhar
Filip Železný
Steven Schockaert
Ondřej Kuželka
Publisher: Springer International Publishing
Book: Inductive Logic Programming
Print ISBN: 978-3-319-63341-1

Electronic ISBN: 978-3-319-63342-8

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-63342-8_9

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