Entity classification method and system and computer readable storage medium

Abstract

The invention discloses an entity classification method and system and a computer readable storage medium. The method comprises the following steps that a type tree of a knowledge base is obtained, feature vectors of to-be-classified entities in the knowledge base are generated, the feature vectors comprise text feature vectors, non-text feature vectors and category feature vectors, and the category feature vectors are generated based on the type tree; and classifying the to-be-classified entities according to the feature vectors of the to-be-classified entities by utilizing a preset classification model to obtain category paths of the to-be-classified entities in the type tree. According to the entity classification method provided by the embodiment of the invention, when entity classification is carried out, the hierarchical structure relationship between the entity categories is utilized, so that layer-by-layer classification of the to-be-classified entities from the coarse granularity type to the fine granularity type can be realized, and the entity classification performance is improved.

Description

technical field [0001] The present invention relates to the field of knowledge graphs, in particular to an entity classification method, system and computer-readable storage medium. Background technique [0002] In recent years, with the rapid development of big data and artificial intelligence, Knowledge Graph (KG) has attracted widespread attention from academia and industry with its powerful data description capabilities. [0003] The construction process of knowledge map mainly includes three steps: information extraction (entity extraction, relation extraction, attribute extraction), knowledge fusion and knowledge processing, and entity classification plays a very important role in the process of building knowledge map. However, traditional entity classification only focuses on coarse-grained categories such as people, organizations, and locations, and the amount of information expressed is often too small, which makes entity classification performance low. Contents o...

AI Technical Summary

Problems solved by technology

However, the traditional entity classification only focuses on coarse-grained categories such as people, organizations, and places, and the amount of information expressed is often too small, which makes the performance of entity classification low.

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