Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base
Abstract
We consider the problem of embedding entities and relations of knowledge bases into low-dimensional continuous vector spaces (distributed representations). Unlike most existing approaches, which are primarily efficient for modelling pairwise relations between entities, we attempt to explicitly model both pairwise relations and long-range interactions between entities, by interpreting them as linear operators on the low-dimensional embeddings of the entities. Therefore, in this paper we introduces Path-Ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it 'structured embedding via pairwise relation and long-range interactions' (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings.
Cite
Text
Wu et al. "Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9391Markdown
[Wu et al. "Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/wu2015aaai-structured/) doi:10.1609/AAAI.V29I1.9391BibTeX
@inproceedings{wu2015aaai-structured,
title = {{Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base}},
author = {Wu, Fei and Song, Jun and Yang, Yi and Li, Xi and Zhang, Zhongfei (Mark) and Zhuang, Yueting},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {1663-1670},
doi = {10.1609/AAAI.V29I1.9391},
url = {https://mlanthology.org/aaai/2015/wu2015aaai-structured/}
}