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

Question Answering on Wikidata

This is an accompanying repository for our COLING 2018 paper (pdf). It contains the code to provide additional information on the experiments and the models described in the paper.

We are working on improving the code to make it easy to exactly replicate the experiments and apply to new question answering data. We also plan to release a separate implementation of the gated graph neural networks.

Disclaimer:

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

Please use the following citation:

@InProceedings{C18-1280,
  author = 	"Sorokin, Daniil
		and Gurevych, Iryna",
  title = 	"Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering",
  booktitle = 	"Proceedings of the 27th International Conference on Computational Linguistics",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"3306--3317",
  location = 	"Santa Fe, New Mexico, USA",
  url = 	"http://aclweb.org/anthology/C18-1280"
}

Paper 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.

Please, refer to the paper for more the model description and training details.

Contacts:

If you have any questions regarding the code, please, don't hesitate to contact the authors or report an issue.

• Daniil Sorokin, <lastname>@ukp.informatik.tu-darmstadt.de
• https://www.informatik.tu-darmstadt.de/ukp/ukp_home/
• https://www.tu-darmstadt.de

Project structure:

File	Description
configs/	Configuration files for the experiments
questionanswering/construction	Base classes for semantic graphs
questionanswering/datasets	Datasets IO
questionanswering/grounding	Grounding graphs in KBs
questionanswering/models	Model definition and training scripts
questionanswering/preprocessing	Mapping data sets to Wikidata
resources/	Necessary resources

Requirements:

• Python 3.6
• PyTorch 0.3.0 - read here about installation
• See requirements.txt for the full list of packages

Running the experiments from the paper:

[Coming soon]

Using the pre-trained model:

[Coming soon]

License:

• Apache License Version 2.0