ICML 2017poster514 citations
Analogical Inference for Multi-relational Embeddings
Hanxiao Liu, Yuexin Wu, Yiming Yang
Abstract
Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the
BibTeX
@InProceedings{pmlr-v70-liu17d,
title = {Analogical Inference for Multi-relational Embeddings},
author = {Hanxiao Liu and Yuexin Wu and Yiming Yang},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {2168--2178},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/liu17d/liu17d.pdf},
url = {https://proceedings.mlr.press/v70/liu17d.html},
abstract = {Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the