{"id":168302,"date":"2015-07-28T00:00:00","date_gmt":"2015-07-28T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/semantic-parsing-via-staged-query-graph-generation-question-answering-with-knowledge-base\/"},"modified":"2018-10-16T20:13:56","modified_gmt":"2018-10-17T03:13:56","slug":"semantic-parsing-via-staged-query-graph-generation-question-answering-with-knowledge-base","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/semantic-parsing-via-staged-query-graph-generation-question-answering-with-knowledge-base\/","title":{"rendered":"Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base"},"content":{"rendered":"
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We propose a novel semantic parsing framework for question answering using a knowledge base. We define a query graph that resembles subgraphs of the knowledge base and can be directly mapped to a logical form. Semantic parsing is reduced to query graph generation, formulated as a staged search problem. Unlike traditional approaches, our method leverages the knowledge base in an early stage to prune the search space and thus simplifies the semantic matching problem. By applying an advanced entity linking system and a deep convolutional neural network model that matches questions and predicate sequences, our system outperforms previous methods substantially, and achieves an F1 measure of 52.5% on the WebQuestions dataset.<\/p>\n<\/div>\n