Question and answer method, question and answer system, electronic equipment and storage medium

Abstract

The invention discloses a question and answer method and system, electronic equipment and a storage medium, and relates to the technical field of knowledge maps. The specific implementation scheme isas follows: receiving input problem information; analyzing the problem information to obtain a plurality of keywords in the problem information; obtaining a keyword attribute list according to the plurality of keywords, wherein the keyword attribute list has a target attribute corresponding to each keyword; obtaining a candidate list of the question information, the candidate list having a plurality of answer options; and according to the target attribute corresponding to each keyword in the keyword attribute list, screening the plurality of answer options in the candidate list to obtain a correct answer. According to the knowledge base question-answering method based on decision screening, decision screening can be conducted on the answer options according to the keyword attributes by referring to the thought of the exclusion method, so that question answering of choice questions of complex knowledge reasoning types can be simplified through the knowledge base question-answering method based on decision screening, the accuracy is guaranteed, and the recall rate of correct answers is increased.

Description

technical field [0001] The present application relates to the technical field of knowledge graphs, and in particular to a question answering method, question answering system, electronic equipment and storage media. Background technique [0002] In related technologies, when constructing a knowledge base-based question answering system in the medical field, commonly used methods are knowledge query-based KBQA (Knowledge Base Question Answering, knowledge base question answering) method and probabilistic reasoning-based KBQA method. [0003] However, the current problems are: the KBQA method based on knowledge query is only suitable for problems with single attribute constraints. When the problem is long and involves multiple attributes, the query recall is low and the trigger is less; For probabilistic knowledge graph data, the construction cost of the knowledge base is relatively high, and when the problem involves multiple attributes, multi-level reasoning is required. Due...

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Problems solved by technology

[0003] However, the current problems are: the KBQA method based on knowledge query is only suitable for problems with single attribute constraints. When the problem is long and involves multiple attributes, the query recall is low and the trigger is less; For probabilistic knowledge graph data, the cost of knowledge base construction is relatively high, and when the problem involves multiple attributes, multi-level reasoning is required. Due to the large number of reasoning relationships, the strategy is more complicated, and the accuracy rate will decrease as the number of reasoning levels increases.

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