Method and system for acquiring answers of tax business questions based on semantic graphs

Abstract

The invention discloses a method and a system for acquiring answers of tax business questions based on semantic graphs, and belongs to the technical field of intelligent questions and answers. The method comprises the steps of defining a question Q and an answer of the question Q according to a state transition graph G, and mapping a semantic interpretation process of the question Q into a sub-graph sub-G query process of the state transition graph G; generating a logic expression; action transfer and conditions in the logic expression are repeated; and obtaining a tax business problem, inputting the tax business problem into the model, checking whether the tax business problem completes the logic expression X, and outputting a tax business problem answer when the tax business problem completes the logic expression X. The semantic parsing process is mapped into the retrieval process of the query graph, so that the retrieval space of problem parsing is reduced, the operation between entities is judged by using the deep convolutional neural network, and the predicate synonym ambiguity problem mentioned in the preamble is solved.

Description

technical field [0001] The present invention relates to the technical field of intelligent question answering, and more specifically relates to a method and system for obtaining answers to taxation business questions based on semantic graphs. Background technique [0002] Artificial intelligence technology has promoted the development of the field of intelligent question answering, especially the advancement of semantic analysis technology has made it possible to recognize more accurate intentions in the process of human-computer dialogue. Intent recognition is particularly important in intelligent question answering services in specific fields. Due to the business relevance of professional field knowledge and the lack of large-scale field data support, effective training data sets cannot be formed. These reasons make it impossible to directly use the current mature Semantic parsing technology, so intent recognition for professional fields is one of the urgent problems to be...

AI Technical Summary

Problems solved by technology

However, these mainstream semantic parsing methods are usually separated from the specific knowledge base, so they encounter many challenges when applied to domain-specific question answering.

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