Association rule transfer learning method based on Markov logic network

Abstract

The invention relates to an association rule transfer learning method based on a Markov logic network. An algorithm of transferring an MLN structure from a source domain to a target domain mainly comprises two parts: firstly, mapping the MLN structure in the source domain to the target domain to establish association between the two domains; and then, optimizing the mapped structure to adapt to indexes of the target domain. The method has the effects that not only can the huge Markov network be concisely described, but also modular knowledge can be flexibly fused into the Markov network. Moreover, imperfection and contradictoriness in a knowledge domain can be tolerated. The algorithm speed is improved. The searching space is restrained by limiting the quantity of updated clauses and updating types of the clauses.

Description

technical field [0001] The invention relates to a statistical relationship learning method combining Markov network and first-order logic, in particular to a method for learning transfer of association rules based on Markov logic network. Background technique [0002] According to the similarity between different tasks, transfer learning transfers the source domain data to the target domain, realizes the utilization of existing knowledge, makes the traditional learning from scratch become accumulative learning, and improves the learning efficiency. The characteristic is to use knowledge in related fields to help complete the learning tasks in the target field. There are many ways to express relevant knowledge in the source domain and the target domain, which can be divided into sample instances, feature maps, model parameters, and association rules. Choosing an appropriate transfer learning method for different knowledge representation methods is a prerequisite for ensuring ...

AI Technical Summary

Problems solved by technology

In 2005, the article "Discriminative training of Markov logic networks" published by Singla et al. proposed a parameter learning method for discriminative training to solve the problem of unsatisfactory inference results between non-adjacent variables in pseudo-likelihood parameter learning.

[0009] At present, there are not many research results on the transfer learning of association rules. The existing methods often have the disadvantage of complex relation expression, which leads to excessive time complexity and high transfer cost.

Images