@inproceedings{komninos-manandhar-2017-feature,
title = "Feature-Rich Networks for Knowledge Base Completion",
author = "Komninos, Alexandros and
Manandhar, Suresh",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2051",
doi = "10.18653/v1/P17-2051",
pages = "324--329",
abstract = "We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed models can better incorporate side information than previously proposed combinations of bilinear models with convolutional neural networks, showing large improvements when scoring the plausibility of unobserved facts with associated textual mentions.",
}
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%0 Conference Proceedings
%T Feature-Rich Networks for Knowledge Base Completion
%A Komninos, Alexandros
%A Manandhar, Suresh
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F komninos-manandhar-2017-feature
%X We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed models can better incorporate side information than previously proposed combinations of bilinear models with convolutional neural networks, showing large improvements when scoring the plausibility of unobserved facts with associated textual mentions.
%R 10.18653/v1/P17-2051
%U https://aclanthology.org/P17-2051
%U https://doi.org/10.18653/v1/P17-2051
%P 324-329
Markdown (Informal)
[Feature-Rich Networks for Knowledge Base Completion](https://aclanthology.org/P17-2051) (Komninos & Manandhar, ACL 2017)
ACL
- Alexandros Komninos and Suresh Manandhar. 2017. Feature-Rich Networks for Knowledge Base Completion. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 324–329, Vancouver, Canada. Association for Computational Linguistics.