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Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

This paper presents a method for question answering with knowledge bases using staged query graph generation.

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Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

By Wen-tau Yih, Ming‐Wei Chang, Xiaodong He, Jianfeng Gao
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The authors propose a semantic parsing approach that generates query graphs in stages to answer questions based on a knowledge base. This method improves the accuracy of question answering by iteratively refining the query graph.

The proposed approach is evaluated on several benchmarks and shows competitive results compared to state-of-the-art methods.

Abstract

The authors propose a semantic parsing approach that generates query graphs in stages to answer questions based on a knowledge base. This method improves the accuracy of question answering by iteratively refining the query graph. The proposed approach is evaluated on several benchmarks and shows competitive results compared to state-of-the-art methods.

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semantic parsingquestion answeringknowledge basequery graph generationnatural language processingKnowledge GraphsStructured ContentAI AgentsLarge Language Models
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