Cowen-Rivers, AI;
Minervini, P;
Rocktaschel, T;
Bosnjak, M;
Riedel, S;
Wang, J;
(2019)
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings.
In: Doran, Derek and Garcez, Artur d’Avila and Lecue, Freddy, (eds.)
Proceedings of the 2019 International Workshop on Neural- Symbolic Learning and Reasoning.
Annual workshop of the Neural-Symbolic Learning and Reasoning Association: Macao, China.
(In press).
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Abstract
Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions. The new framework results in a highly-scalable method. Under a Bernoulli sampling framework, we provide an alternative justification for commonly used techniques in large-scale stochastic variational inference, which drastically reduce training time at a cost of an additional approximation to the variational lower bound. We introduce two models from this highly scalable probabilistic framework, namely the Latent Information and Latent Fact models, for reasoning over knowledge graph-based representations. Our Latent Information and Latent Fact models improve upon baseline performance under certain conditions. We use the learnt embedding variance to estimate predictive uncertainty during link prediction, and discuss the quality of these learnt uncertainty estimates. Our source code and datasets are publicly available online at https://github.com/alexanderimanicowenrivers/Neural-Variational-Knowledge-Graphs.
| Type: | Proceedings paper |
|---|---|
| Title: | Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings |
| Event: | 2019 International Workshop on Neural- Symbolic Learning and Reasoning |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://collegepublications.co.uk/ifcolog/ |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10081240 |
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