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

Understand

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.