Data association method, system and device based on Markov logic network

Abstract

The invention discloses a data association method, system and device based on a Markov logic network. The method includes: utilizing key personnel database, case to be solved and its corresponding weighted rule database to construct the model of obtaining crime probability based on Markov logic network, obtaining the probability of each key personnel committing the case to be solved, thereby screening out the target object, and realizing the data association between the target object and the case to be solved; the method for obtaining the weighted rule base includes: the ontology model view set is constructed by using the data of the solved case which is the same as the main type of the case to be solved, the first-order logic rule set and predicate atom set are extracted, and then the rule weight learning model based on Markov logic network is constructed by using the two extracted sets, the rule weights are obtained by training the model, and thus the weighted rule base composed of the first-order logic rule and the corresponding rule weights is obtained. The acquisition of the rules in the invention does not depend on manpower, and the accuracy rate of data association can be improved.

Description

technical field [0001] The invention belongs to the field of data mining, and more specifically relates to a data association method, system and equipment based on a Markov logic network. Background technique [0002] In recent years, public security cases have continued to occur frequently, seriously affecting the quality of life of residents and social order. Due to limited police resources, traditional methods of solving cases manually can no longer meet the current needs of solving cases. Therefore, using information technology to improve the detection rate has become an urgent need. By associating the data of the target object that needs to be investigated in an unsolved case with the unsolved case, the efficiency of solving the unsolved case can be effectively improved. [0003] The probabilistic graphical model is a general representation and processing method of uncertainty knowledge, which can be used to assist in screening out the target objects that need to be re...

AI Technical Summary

Problems solved by technology

[0004] Although the Markov logic network can implement reasoning by incorporating domain rules into the Markov network, the input of domain rules requires the joint participation of a large number of senior experts in related fields.

On the one hand, excessive reliance on manually obtaining domain rules will lead to waste of human resources and low efficiency. On the other hand, domain rules given by domain experts based on experience may include redundant or even wrong rules, and the comprehensiveness of domain rules often is not guaranteed, which can lead to inaccurate results obtained

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