Knowledge graph construction method and device, electronic equipment and storage medium

Abstract

The invention provides a knowledge graph construction method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a plurality of entities to be classified and all HTML data corresponding to each entity; extracting description texts of the corresponding entities from the HTML data of the entities, and constructing a training data set based on the names of all the entities and the description texts of all the entities; training a model by using the training data set, and identifying the category to which each entity in a plurality of entities belongs based on the trained model; and constructing the knowledge graph based on the plurality of entities and the category to which each entity belongs. A large amount of type information presented in a network liquid level in an unstructured text form can be extracted, and a model with high accuracy and good effect is trained to classify entities. And based on massive data, constructing a comprehensive and deep knowledge graph which is formed by fusing or linking a plurality of libraries in an open field and a specific field.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular, to a knowledge graph construction method, apparatus, electronic device, and computer-readable storage medium. Background technique [0002] As an important carrier that carries massive underlying knowledge and supports upper-level intelligent applications, knowledge graph plays an extremely important role in the era of intelligence. Limited by the huge difference between unstructured text and structured knowledge, there are still many challenges in automating the construction of knowledge graphs. Usually, building a knowledge graph requires assigning a predefined type to each entity after entity recognition is completed, that is, the "entity type inference" task. The "entity type inference" task is an important step in building a knowledge graph. However, a large amount of type information is presented in web pages in the form of unstructured text, wh...

AI Technical Summary

Problems solved by technology

However, a large number of types of information are presented in the form of unstructured text on web pages, and text processing is difficult. It is still a great challenge to extract results while ensuring high accuracy and coverage

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