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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Notes
- 1.
Established notions such as “rule” are further used also for their weighted analogies.
- 2.
These represent aggregation operators that can take a variable number of arguments.
- 3.
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.
- 4.
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.
- 5.
Downloaded from https://alchemy.cs.washington.edu/data/animals/.
- 6.
Downloaded from https://alchemy.cs.washington.edu/data/nations/ and from https://alchemy.cs.washington.edu/data/umls/.
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Acknowledgments
GS and FZ acknowledge support by project no. 17-26999S granted by the Czech Science Foundation. OK is supported by a grant from the Leverhulme Trust (RPG-2014-164). SS is supported by ERC Starting Grant 637277. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures”.
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Šourek, G., Manandhar, S., Železný, F., Schockaert, S., Kuželka, O. (2017). Learning Predictive Categories Using Lifted Relational Neural Networks. In: Cussens, J., Russo, A. (eds) Inductive Logic Programming. ILP 2016. Lecture Notes in Computer Science(), vol 10326. Springer, Cham. https://doi.org/10.1007/978-3-319-63342-8_9
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