Mapping knowledge domain relation inference method and device, computer equipment, and storage medium

Abstract

The invention relates to a mapping knowledge domain relation inference method and device, computer equipment, and a storage medium. The method comprises the steps: extracting an entity set and a relation set in a mapping knowledge domain, and obtaining a current triple set; extracting a triple, matched with a predefined knowledge rule, from the current triple set, and obtaining a current trainingset; Training a current teaching model through a first loss function according to the current training set, enabling the output of the teaching model to be fit with the output of a current learning model, and obtaining a trained teaching model and an updated current triple set; training the current learning model through a second loss function according to the updated current triple set, enablingthe output of the current learning model to be fit with the output of the current teaching model, obtaining a trained current learning model and an updated current training set, carrying out the training repeatedly till a training result meets a condition of convergence, and obtaining a target learning model; obtaining a target entity, and carrying out the inference according to the target learning model to obtain an inference result.

Description

technical field [0001] The present application relates to the technical field of knowledge graphs, in particular to a knowledge graph relationship reasoning method, device, computer equipment and storage medium. Background technique [0002] A knowledge graph is a knowledge cluster organized in the form of a graph in knowledge engineering, which consists of different types of entities as nodes and relationships as edges connecting nodes. In the knowledge graph, entities refer to objective objects in the real world, or abstract concepts in human thought. Relationships describe the actual relationship between two entities. [0003] In traditional technology, when machine learning is used to train models for relational reasoning, the trained models are usually based on knowledge bases in specific fields for model training, and because the similarity characteristics of data in various fields are not the same, resulting in a certain The model trained in the domain is difficult ...

AI Technical Summary

Problems solved by technology

[0003] In traditional technology, when machine learning is used to train a model for relational reasoning, the trained model is usually based on the knowledge base of a specific field for model training, and because the similarity characteristics of data between various fields are not the same, resulting in a certain The model trained in the domain is difficult to transfer to another domain, so that the trained model is not effective for relational reasoning

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