Feature-Rich Networks for Knowledge Base Completion

Alexandros Komninos, Suresh Manandhar


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.
Anthology ID:
P17-2051
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
324–329
Language:
URL:
https://aclanthology.org/P17-2051
DOI:
10.18653/v1/P17-2051
Bibkey:
Cite (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.
Cite (Informal):
Feature-Rich Networks for Knowledge Base Completion (Komninos & Manandhar, ACL 2017)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/P17-2051.pdf