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http://dx.doi.org/10.3745/KTSDE.2020.9.8.243

Bilinear Graph Neural Network-Based Reasoning for Multi-Hop Question Answering  

Lee, Sangui (경기대학교 컴퓨터과학과)
Kim, Incheol (경기대학교 컴퓨터과학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.9, no.8, 2020 , pp. 243-250 More about this Journal
Abstract
Knowledge graph-based question answering not only requires deep understanding of the given natural language questions, but it also needs effective reasoning to find the correct answers on a large knowledge graph. In this paper, we propose a deep neural network model for effective reasoning on a knowledge graph, which can find correct answers to complex questions requiring multi-hop inference. The proposed model makes use of highly expressive bilinear graph neural network (BGNN), which can utilize context information between a pair of neighboring nodes, as well as allows bidirectional feature propagation between each entity node and one of its neighboring nodes on a knowledge graph. Performing experiments with an open-domain knowledge base (Freebase) and two natural-language question answering benchmark datasets(WebQuestionsSP and MetaQA), we demonstrate the effectiveness and performance of the proposed model.
Keywords
Open Domain Question Answering; Knowledge Graph; Complex Question; Graph Neural Network;
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