Case implicit knowledge mining method and device and electronic equipment

Abstract

The invention discloses a case implicit knowledge mining method and device and electronic equipment, and the method comprises the steps: constructing an initial case knowledge graph of target case information, and the target case information comprises case text information and / or legal information; based on a pre-created ontology library, performing rule reasoning on the initial case knowledge graph to obtain a first implicit information set; based on a pre-generated graph neural network model, performing information prediction on the initial case knowledge graph to obtain a second implicit information set; and processing the first implicit information set and the second implicit information set to obtain target implicit information. The purpose of improving the calculation efficiency and the information mining accuracy is achieved.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a method, device and electronic equipment for mining implicit knowledge of a case. Background technique [0002] Knowledge graph is an important direction of artificial intelligence, which is widely used in search engines, intelligent question answering, information recommendation, etc. In recent years, a large number of applications of knowledge graphs in vertical fields have emerged, among which legal knowledge graphs and legal knowledge engineering are one of them. At the same time, how to apply the legal knowledge map and case knowledge map to the field of smart justice, so as to promote the auxiliary application of judicial functions, such as the intelligent application of procuratorial knowledge question and answer, similar case push, and associative search of laws and regulations, has become a smart application. Consensus in Forensic Research and Computatio...

AI Technical Summary

Problems solved by technology

[0004] At present, mining implicit knowledge in knowledge graphs can be reasoned by using frequent patterns, constraints or paths at the abstract ontology level, but this method needs to be instantiated, and the computable type is relatively large. For knowledge graphs with a large number of instances, it is difficult to Computational resources are large and the efficiency is low; there is also a method of using the neural network model for prediction, but in the case of small samples, the model training is difficult to converge

It can be seen that the traditional processing method will make the processing efficiency low, and the accuracy of the processing result is poor

Images