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Graph Neural Networks for Knowledge Base Question Answering

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If you have a question about this talk, please contact Andrew Caines .

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In this talk, we present a semantic parsing approach to Knowledge Base Question Answering. 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 will present a formulation of Gated Graph Neural Networks for labeled knowledge base subgraphs and show how it can be used in a question answering pipeline. Empirically, we demonstrate on two data sets that the graph networks outperform the baseline models that do not explicitly model the semantic structure.

This talk is part of the NLIP Seminar Series series.

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FW11, William Gates Building. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon SW01 ki287

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