Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering

Daniil Sorokin, Iryna Gurevych


Abstract
The most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist of multiple entities and relations. Previous work largely focused on selecting the correct semantic relations for a question and disregarded the structure of the semantic parse: the connections between entities and the directions of the relations. We propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. We show on two data sets that the graph networks outperform all baseline models that do not explicitly model the structure. The error analysis confirms that our approach can successfully process complex semantic parses.
Anthology ID:
C18-1280
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3306–3317
Language:
URL:
https://aclanthology.org/C18-1280
DOI:
Bibkey:
Cite (ACL):
Daniil Sorokin and Iryna Gurevych. 2018. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3306–3317, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering (Sorokin & Gurevych, COLING 2018)
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PDF:
https://aclanthology.org/C18-1280.pdf
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