Training method and device of entity classification model, and entity classification method and device

By constructing an entity classification model and utilizing a sample training set and a hierarchical comparison strategy rich in categories, the problem of low accuracy in fine-grained entity classification in knowledge graphs is solved, and efficient identification and classification of fine-grained entities is achieved.

CN115270990BActive Publication Date: 2026-05-26TENCENT TECH WUHAN
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TENCENT TECH WUHAN
Filing Date
2022-08-11
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies have low accuracy in fine-grained entity classification in knowledge graphs and lack effective modeling of category differences, resulting in poor performance in identifying similar entities and refining entity domains.

Method used

By acquiring a sample training set, including original sample sentences, sample entities and their coarse and fine granular category labels, an entity classification model is constructed. The model is trained using the first and second sample sentences, guiding the model to learn the correct entity category. Furthermore, through a category-rich hierarchical comparison strategy and descriptive information, the accuracy of the model in fine-grained classification is improved.

Benefits of technology

It improves the entity classification model's ability to identify fine-grained entities, enhances its ability to distinguish similar multi-level entity categories and identify different levels of categories, and improves the quality of knowledge graph construction.

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Abstract

This application provides a fine-grained entity classification method, apparatus, and electronic device for knowledge graph construction, relating to the field of machine learning in artificial intelligence. The fine-grained entity classification method includes: acquiring a statement to be classified; the statement to be classified includes entities to be classified; acquiring a first statement based on the entities to be classified, the statement to be classified, and preset annotation information; performing entity classification prediction on the first statement using an entity classification model to obtain a fine-grained entity classification result corresponding to the statement to be classified; the entity classification model is trained as follows: acquiring a sample training set; sample category labels include coarse-grained category labels and fine-grained category labels; acquiring a first sample statement and a second sample statement corresponding to the original sample statement; the first sample statement includes the original sample statement and annotation information; the second sample statement includes the original sample statement and fine-grained category labels; and training the initial classification model to obtain an entity classification model.
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