COMBINED PRODUCT MINING METHOD BASED ON KNOWLEDGE GRAPH RULE EMBEDDING

Disclosed in the present invention is a combined product mining method based on knowledge graph rule embedding, comprising: expressing rules, products, attributes, and attribute values as embedding; splicing the embedding of the rules and attributes and then and inputting same into a first neural network to obtain importance scores of the attributes; splicing the rules and attributes and inputting same into a second neural network to obtain the embedding of the attribute values that should be under the attributes according to the rules; calculating the similarity between the values of two input products under the attributes and the embedding of the attribute values calculated by a model; calculating and aggregating the scores of all attribute-attribute value pairs to obtain the scores of the two products under the rules; and then calculating the cross-entropy loss for the real scores of the two products, and performing iteratively training using a gradient descent-based optimization algorithm. After the model