Multi-triad joint extraction method based on knowledge graph embedding

Abstract

The invention discloses a multi-triad joint extraction method based on knowledge graph embedding, comprising the following steps of: processing an acquired text statement to obtain a text statement matrix; inputting the text statement matrix into a Transformer model to extract semantic information of text statements to obtain semantic feature vectors; applying the semantic feature vectors to an entity recognition sequence labeling task to obtain entity recognition cross entropy loss loss1; applying the semantic feature vector to a relationship classification task, and solving entity recognition cross entropy loss loss2 of relationship classification; constructing an entity word relationship by utilizing an entity labeling prediction matrix and a statement entity word relationship classification matrix, and solving cross entropy loss loss3 of the relationship; calculating a minimized total loss function loss by utilizing an optimization algorithm based on gradient descent of the loss1,the loss2 and the loss3; and obtaining a trained Transformer model according to the text statement to be predicted, inputting the text statement to be predicted into the trained Transformer model to obtain a predicted semantic feature vector of the predicted text statement, and completing a multi-triad joint extraction method.

Description

technical field [0001] The invention relates to the technical field of data storage and processing, in particular to a triple extraction method in a knowledge map. Background technique [0002] The knowledge graph describes the concepts, entities and their relationships in the objective world in a structured form, expresses the information of the Internet in a form closer to the human cognitive world, and provides a way to better organize, manage and understand the massive information on the Internet. Capabilities, the knowledge graph mainly includes entities, relationships, and triples, and each triple represents a piece of knowledge. When there is a certain relationship between two entities, use (h, r, t) to represent a triplet, where h, t represent the head entity and tail entity respectively, and r represents the relationship, for example (China, capital, Beijing ) means the knowledge that "Beijing is the capital of China". [0003] Entity relationship learning is to a...

AI Technical Summary

Problems solved by technology

[0007] The present invention provides a multi-triple joint extraction method based on knowledge map embedding. The model training stage introduces knowledge map embedding under knowledge representatio...

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