Open Access

ARTICLE

Intelligent Spatial Anomaly Activity Recognition Method Based on Ontology Matching

Longgang Zhao¹, Seok-Won Lee^1,2,*

1 The Knowledge-intensive Software Engineering (NiSE) Research Group, Department of Artificial Intelligence, Ajou University, Suwon City, 16499, Republic of Korea
2 Department of Software and Computer Engineering, Ajou University, Suwon City, 16499, Republic of Korea

* Corresponding Author: Seok-Won Lee. Email:

Computers, Materials & Continua 2025, 83(3), 4447-4476. https://doi.org/10.32604/cmc.2025.063691

Received 21 January 2025; Accepted 18 April 2025; Issue published 19 May 2025

Abstract

This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency. The method in this paper adopts the event sequence segmentation technique, combines location awareness with time interval reasoning, and improves human activity recognition through ontology reasoning. Compared with the existing methods, the framework performs better when dealing with uncertain data and complex scenes, and the experimental results show that its recognition accuracy is improved by 15.6% and processing time is reduced by 22.4%. In addition, it is found that with the increase of context complexity, the traditional ontology inference model has limitations in abnormal behavior recognition, especially in the case of high data redundancy, which tends to lead to a decrease in recognition accuracy. This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.

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Keywords

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