Complex question knowledge base question answering method based on embedded and candidate subgraph pruning

Abstract

The invention discloses a complex question knowledge base question answering method based on embedded and candidate subgraph pruning, and belongs to the technical field of data processing. The complexity of a relation is distinguished based on dependency syntactic analysis, and a candidate subgraph range is preliminarily screened out; the candidate sub-graphs are pruned through a pruning method based on tail entities and relation types to reduce interference brought by error paths in the candidate sub-graphs during model training; a short text matching model based on a neural network is trained, so that the matching score of a question and a correct question and answer path context is relatively high. When a new question and answer data set is constructed by SPE-QA, the complexity degree of a relationship in the question is analyzed based on a dependency syntax, and a candidate subgraph range is preliminarily screened out; a relation path type selector is trained, and the candidate sub-graphs is further pruned; and a short text matching model based on the neural network is constructed, so that the matching score of the question and the correct question and answer path context is relatively high.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a complex question knowledge base question and answer method based on embedded and candidate subgraph pruning. Background technique [0002] Usually, when a natural language question is given, the knowledge base question answering system performs semantic understanding and analysis through the question, and then uses the knowledge base to query and reason to obtain the answer. [0003] According to the number of knowledge graph triples, natural language problems are divided into two types: [0004] (1) Single-relational questions, which only rely on a triple to complete the question and answer; [0005] (2) Multi-relational questions require at least two triples; answering multi-relational questions is still challenging due to the diversity of natural languages ​​and the complexity of candidate answers. [0006] For the question answering of single-relations...

AI Technical Summary

Problems solved by technology

[0008] Purpose of the invention: In order to solve the deficiencies in the knowledge graph question answering of current complex problems, the purpose of the present invention is to provide a complex problem knowledge base question answering method based on embedded and candidate subgraph pruning, which can reduce the scope of candidate subgraphs, and in Obtaining higher accuracy on partial multi-relational question answering

Images