Markov logic network-based knowledge mapping relationship type speculation method and device

Abstract

The invention relates to a Markov logic network-based knowledge mapping relationship type speculation method and device. The device comprises an inference rule obtaining module, a credibility weight learning module, a probability inference module and a relationship type determination module, wherein the inference rule obtaining module is used for generating inference rules according to path features of known nodes of a data knowledge mapping; the credibility weight learning module is used for carrying out credibility weight learning on the inference rules through a Markov logic network and obtaining inference rules with weights; the probability inference module is used for carrying out probability inference on relationship types existing among to-be-speculated nodes according to the inference rules with the weights, so as to obtain relationship type probability among the to-be-speculated nodes; and the relationship type determination module is used for selecting a relationship type with a relatively large probability value as a relationship type among the to-be-speculated nodes according to the relationship type probability obtained by the probability inference module. According to the method and device, the automatic learning of the inference rules in the knowledge mapping and the probability inference of relationship types among nodes can be realized, so that the correctness of speculating the relationship types which possibly exist among the nodes can be effectively ensured.

Description

technical field [0001] The invention belongs to the technical field of big data analysis, and in particular relates to a Markov logic network-based method for inferring relation types of knowledge graphs and a device thereof. Background technique [0002] With the gradual transformation of the Internet from the document World Wide Web to the data World Wide Web, the interconnected entity objects in the Internet are gradually transformed into knowledge graphs that can be understood by computers. Quick questions and answers, related queries, and entity recommendations based on knowledge graphs provide people's lives. Great convenience. However, the construction of a knowledge graph is a complex process. The relationship between entities is often difficult to fully obtain in the process of knowledge extraction. How to infer the unknown relationship types between entities based on the existing relationship types between entities, so as to improve the knowledge graph. It has ver...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies in the prior art, the present invention provides a Markov logic network-based method for inferring relationship types in knowledge graphs and its device, which realizes automatic learning of inference rules in knowledge graphs and probabilistic reasoning of relationship types between nodes, and solves the problem of perfecting knowledge graphs. In the process, artificially formulating inference rules is unscientific, not comprehensive, and has poor scalability. It has high credibility and effectively improves the accuracy of speculation.

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