Knowledge reasoning method and system of knowledge graph based on meta-knowledge and medium

Abstract

The invention provides a knowledge inference method and system of a knowledge graph based on meta-knowledge and a medium, the training difficulty does not significantly increase with the increase of the scale of the knowledge graph, the inference accuracy is more advantageous, and the method comprises the following steps: step 1, data set construction: establishing a data set composed of knowledgegraph triples: a head entity, a relationship and a tail entity; step 2, data re-processing: re-learning the initialized embedded vectors of the entities in the data set through a data re-processing model to obtain semantics of the entities and obtain final embedded vectors of the entities; step 3, model training: constructing a knowledge inference model, and inputting triples in the data set subjected to data reprocessing into the knowledge inference model for model training to obtain a trained knowledge inference model; and step 4, knowledge reasoning: scoring the alternative tail entities in the knowledge graph triad through the trained knowledge reasoning model, and selecting the tail entity with the highest score as a reasoning result of the knowledge reasoning model.

Description

technical field [0001] The present invention relates to the technical field of knowledge reasoning, in particular to a knowledge reasoning method, system and medium based on meta-knowledge knowledge graphs. Background technique [0002] In recent years, knowledge reasoning technology based on machine learning has developed rapidly and achieved remarkable results. In essence, it uses the triples in the knowledge map as training data for modeling, and uses statistical learning methods to extract some logic from it. Rules, in order to reason or judge new knowledge. However, due to the limitations of the inherent conditions of the machine learning model, the knowledge reasoning technology based on this method generally has two problems. One is that the training of the knowledge reasoning model requires a large number of samples, and the model training is more complex; the other is Because large-scale sparse knowledge graphs often have unbalanced data distribution, the existing ...

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Problems solved by technology

However, due to the limitations of the inherent conditions of the machine learning model, the knowledge reasoning technology based on this method generally has two problems. One is that the training of the knowledge reasoning model requires a large number of samples, and the model training is more complex; the other is Due to the unbalanced distribution of data in large-scale sparse knowledge graphs, the existing knowledge reasoning technology is not very effective in reasoning on such large-scale sparse knowledge graphs.

[0003] The GMatching model is the first to apply the idea of ​​meta-learning to knowledge reasoning, which solves the above two problems, but there is still room for improvement. First, the data reprocessing part of the model does not express the relationship semantics between entities. Incomplete; the second is the reasoning part of the model, the extraction of relational features in the reasoning process lacks pertinence

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