Knowledge graph hyponymy relation identification method based on mode expansion and BERT classification and storage device

Abstract

The invention relates to the technical field of text recognition, in particular to a knowledge graph hyponymy relation recognition method based on pattern expansion and BERT classification and a storage device. The knowledge graph hyponymy relation recognition method based on pattern expansion and BERT classification comprises the steps: extracting potential hyponymy relation pairs from external data based on pattern expansion, and forming a hyponymy relation model training seed corpus in combination with a preset resource library; obtaining a to-be-predicted hyponymy data set, and predictingthe to-be-predicted data set based on the BERT-Attention-Bi-LSTM model to obtain a hyponymy prediction result; and further processing the hyponymy relationship prediction result through a preset ruleto obtain a final hyponymy relationship prediction result. The method greatly reduces the complexity and labor investment of rule compilation, has stronger realizability compared with another mainstream statistics-based hyponymy concept identification method, and can provide technical support for the construction of various professional knowledge maps.

Description

technical field [0001] The invention relates to the technical field of text recognition, in particular to a method and a storage device for identifying the hyponym relationship of a knowledge map based on pattern expansion and BERT classification. Background technique [0002] With the rapid development of Internet software and hardware related technologies, people are gradually entering the intelligent age from the information age. As an important carrier that carries the massive knowledge at the bottom layer and supports intelligent applications at the upper layer, the knowledge graph plays an extremely important role in the intelligent era. However, limited by the huge difference between unstructured text and structured knowledge, there are still many challenges in automatically constructing knowledge graphs and using knowledge graphs to support upper-level applications. Therefore, it is necessary to conduct research on the construction of knowledge graph and its core ap...

AI Technical Summary

Problems solved by technology

[0013] To this end, it is necessary to provide a method for identifying the hyponymy relationship of knowledge graphs based on pattern expansion and BERT classification, which is used to solve the problem that the existing knowledge graph hyponymy relationship identification methods rely heavily on professional knowledge and rule-making capabilities, requiring investment A lot of labor, relatively high demand for prior knowledge bases, etc.

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