Method, device and medium for mining character relationships based on knowledge graph embedding

Abstract

The present invention provides a method, device, and medium for mining character relationships based on knowledge graph embedding, which can better express the relationship between entity space and relational space in semantic space, thereby more accurately mining the hidden mutual relationships between characters. Relationships and suspicious relationships, including the steps of: extracting original entities and original relationships from a knowledge map with person relationships, embedding original relationships and embedding original entities interactively to obtain interaction units; constructing and training a prediction model, the prediction model includes Feedforward neural network layer, convolutional neural network layer and scoring layer, after embedding the interaction unit, input the feedforward neural network layer and convolutional neural network layer respectively to obtain the predicted entity embedding, and input the predicted entity embedding into the scoring Layer, obtain the score of the entity embedding predicted by the scoring layer; the score of the predicted entity embedding with the highest score is output as the mined character relationship.

Description

technical field [0001] The present invention relates to the field of knowledge graphs, in particular to a method, device and medium for mining character relationships based on knowledge graph embedding. Background technique [0002] Knowledge graphs are playing an important role in detecting and preventing crime. The knowledge graph contains a large number of existing facts, and each fact forms a triplet (h, r, t), including the head entity h, tail entity t and relation r. Although a large number of facts have been discovered in knowledge graphs, they are still incomplete and limited to our cognition. In order to solve this problem, the task of knowledge graph completion is to predict a speculative fact, that is, to predict a speculative element in a triple, so that the technology can further expand the knowledge graph on the basis of existing facts, so that it can be used to reveal Hidden interrelationships and suspicious relationships between characters to discover and p...

AI Technical Summary

Problems solved by technology

Due to the lack of adequate relational embedding learning, these knowledge graph embedding methods are difficult to extract semantic features in the relational space

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