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Open Access

Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Computer School, University of South China, Hengyang 421001, China
School of Computing, Ulster University, Belfast, BT15 1AP, UK
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Abstract

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.

Keywords

structure learningMarkov Logic Network (MLN)rulebase constructionprobabilistic latent semantics

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Tsinghua Science and Technology
Volume 28 Issue 5,
October 2023
Pages 952-964
DOI: 10.26599/TST.2022.9010072
Cite this article:
Cui S, Zhu T, Zhang X, et al. Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis. Tsinghua Science and Technology, 2023, 28(5): 952-964. https://doi.org/10.26599/TST.2022.9010072

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Received: 10 May 2022
Revised: 10 August 2022
Accepted: 27 November 2022
Published: 19 May 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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