Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

查看全文 作　　者：Shan [1]Cui;Tao [2]Zhu;Xiao [1]Zhang;Liming [3]Chen;Lingfeng [1]Mao;Huansheng [1]Ning 机构地区：[1]School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China;[2]Computer School,University of South China,Hengyang 421001,China;[3]School of Computing,Ulster University,Belfast,BT151AP,UK 出　　处：《Tsinghua Science and Technology》索引2023年第28卷第5期,共13页 基　　金：supported by the National Natural Science Foundation of China(No.61872038). 摘　　要：Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily life.HAR uses sensor devices to collect user behavior data,obtain human activity information and identify them.Markov Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and data.MLN can solve the problems of complexity and uncertainty,and has good knowledge expression ability.However,MLN structure learning is relatively weak and requires a lot of computing and storage resources.Essentially,the MLN structure is derived from sensor data in the current scene.Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be obtained.To this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning.Such a rulebase can reduce the time required for MLN structure learning.We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time.In addition,we evaluate the parameters of the rulebase building scheme to check its stability. 关 键 词：Markov Logic Network(MLN) structure learning rulebase construction probabilistic latent semantics