Effective Blending of Two and Three-Way Interactions for Modeling Multi-Relational Data
Abstract
Much work has been recently proposed to model relational data, especially in the multi-relational case, where different kinds of relationships are used to connect the various data entities. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and jointly fine-tuned. We show that our approach outperforms existing models on different types of relationships, and achieves state-of-the-art results on two benchmarks of the literature.
Cite
Text
García-Durán et al. "Effective Blending of Two and Three-Way Interactions for Modeling Multi-Relational Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44848-9_28Markdown
[García-Durán et al. "Effective Blending of Two and Three-Way Interactions for Modeling Multi-Relational Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/garciaduran2014ecmlpkdd-effective/) doi:10.1007/978-3-662-44848-9_28BibTeX
@inproceedings{garciaduran2014ecmlpkdd-effective,
title = {{Effective Blending of Two and Three-Way Interactions for Modeling Multi-Relational Data}},
author = {García-Durán, Alberto and Bordes, Antoine and Usunier, Nicolas},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {434-449},
doi = {10.1007/978-3-662-44848-9_28},
url = {https://mlanthology.org/ecmlpkdd/2014/garciaduran2014ecmlpkdd-effective/}
}