Remote intervention assistance system for a person in difficulty based on probabilistic ontologies

The proposed theme of this paper is to develop a system for remote intervention assistance with a person in difficulty, which is becoming increasingly important as the world's population ages. The goal is to provide effective long-term care to reduce hospitalizations of the elderly and alleviate the burden of care for hospitals and governments. The system requires careful consideration of costs, resources, and efficiency due to the complexity and heterogeneity of the data involved, making human analysis nearly impossible. To enable machine analysis and decision-making, the proposed system is based on an ontological approach that combines medical, social, temporary, and geographical factors, along with Semantic Web Rule Language (SWRL). The system is further enhanced by the integration of probabilistic reasoning through Probabilistic Web Ontology Language (PR-OWL), which allows for the use of ontological resources in a probabilistic environment. The paper also proposes two additional approaches in the form of Jena Application Programming Interfaces (API), including ontological enrichment based on Bayesian Network (BN) knowledge bases and structural/parametric learning based on ontological knowledge bases, as well as a probabilistic clause integrated into the ontological format. As results, the proposed approaches were tested in a simulation, and feedback was collected from users. The feedback was compared, and an increase in precision was observed from one approach to another with varying execution times. These results demonstrate the effectiveness of the proposed system for remote intervention assistance and provide a foundation for future research and development in this area.

Automated summary

The proposed paper aims to develop a remote intervention assistance system for individuals in difficulty, which is crucial due to the aging population. The system utilizes an ontological approach along with Semantic Web Rule Language (SWRL) and Probabilistic Web Ontology Language (PR-OWL) to enable machine analysis and decision-making. The paper also proposes two additional approaches using Jena Application Programming Interfaces (API) and tests them in a simulation. Feedback from users demonstrates the effectiveness of the system and its potential for future research and development in this field.