KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

Alberto Garcia-Duran, Mathias Niepert
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:371-380, 2018.

Abstract

We present KBLRN, a framework for end-to- end learning of knowledge base representa- tions from latent, relational, and numerical fea- tures. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representa- tions of knowledge bases by integrating la- tent, relational, and numerical features. We show that instances of KBLRN outperform ex- isting methods on a range of knowledge base completion tasks. We contribute a novel data set enriching commonly used knowledge base completion benchmarks with numerical fea- tures. The data sets are available under a per- missive BSD-3 license1. We also investigate the impact numerical features have on the KB completion performance of KBLRN.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-garcia-duran18a, title = {KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features}, author = {Garcia-Duran, Alberto and Niepert, Mathias}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {371--380}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/garcia-duran18a/garcia-duran18a.pdf}, url = {https://proceedings.mlr.press/r16/garcia-duran18a.html}, abstract = {We present KBLRN, a framework for end-to- end learning of knowledge base representa- tions from latent, relational, and numerical fea- tures. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representa- tions of knowledge bases by integrating la- tent, relational, and numerical features. We show that instances of KBLRN outperform ex- isting methods on a range of knowledge base completion tasks. We contribute a novel data set enriching commonly used knowledge base completion benchmarks with numerical fea- tures. The data sets are available under a per- missive BSD-3 license1. We also investigate the impact numerical features have on the KB completion performance of KBLRN.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features %A Alberto Garcia-Duran %A Mathias Niepert %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-garcia-duran18a %I PMLR %P 371--380 %U https://proceedings.mlr.press/r16/garcia-duran18a.html %V R16 %X We present KBLRN, a framework for end-to- end learning of knowledge base representa- tions from latent, relational, and numerical fea- tures. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representa- tions of knowledge bases by integrating la- tent, relational, and numerical features. We show that instances of KBLRN outperform ex- isting methods on a range of knowledge base completion tasks. We contribute a novel data set enriching commonly used knowledge base completion benchmarks with numerical fea- tures. The data sets are available under a per- missive BSD-3 license1. We also investigate the impact numerical features have on the KB completion performance of KBLRN. %Z Reissued by PMLR on 04 October 2026.
APA
Garcia-Duran, A. & Niepert, M.. (2018). KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:371-380 Available from https://proceedings.mlr.press/r16/garcia-duran18a.html. Reissued by PMLR on 04 October 2026.

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