Entity classification method, system and computer readable storage medium

Abstract

The invention discloses a method, system and computer-readable storage medium for entity classification, wherein the method includes the following steps: obtaining the type tree of the knowledge base, and generating a feature vector of the entity to be classified in the knowledge base, wherein the feature vector includes text features Vector, non-text feature vector and category feature vector, the category feature vector is generated based on the type tree; use the preset classification model to classify the entity to be classified according to the feature vector of the entity to be classified, so as to obtain the category path of the entity to be classified in the type tree. The entity classification method in the embodiment of the present invention utilizes the hierarchical relationship between entity categories when performing entity classification, thereby enabling layer-by-layer classification of entities to be classified from coarse-grained types to fine-grained types, improving entity classification. performance.

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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