Modelling relational data using Bayesian clustered tensor factorization
We consider the problem of learning probabilistic models for complex relational structures between various types of objects. A model can help us "understand" a dataset of relational facts in at least two ways, by finding interpretable structure in the data, and by supporting predictions, o...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation,
2017-12-21T14:36:19Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | We consider the problem of learning probabilistic models for complex relational structures between various types of objects. A model can help us "understand" a dataset of relational facts in at least two ways, by finding interpretable structure in the data, and by supporting predictions, or inferences about whether particular unobserved relations are likely to be true. Often there is a tradeoff between these two aims: cluster-based models yield more easily interpretable representations, while factorization-based approaches have given better predictive performance on large data sets. We introduce the Bayesian Clustered Tensor Factorization (BCTF) model, which embeds a factorized representation of relations in a nonparametric Bayesian clustering framework. Inference is fully Bayesian but scales well to large data sets. The model simultaneously discovers interpretable clusters and yields predictive performance that matches or beats previous probabilistic models for relational data. Natural Sciences and Engineering Research Council of Canada Shell Oil Company NTT Communication Science Laboratories United States. Air Force. Office of Scientific Research (FA9550-07-1-0075) United States. Air Force. Office of Scientific Research. Multidisciplinary University Research Initiative |
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