An Ontology for In‐Depth Description of User Situations in Connected Environments

ABSTRACT Context‐awareness is increasingly recognised as a fundamental principle in the development of ubiquitous computing and ambient intelligence. By leveraging contextual data about users and their environments, systems can gain a deeper understanding of the evolving user situation. This empowers them to dynamically adapt their operations, leading to optimised resource utilisation, enhanced decision‐making, and ultimately, greater user satisfaction. However, a critical challenge lies in effectively representing user situations with a high degree of expressiveness. While ontology‐based data models have emerged as a promising approach due to their ability to handle the inherent heterogeneity of context information, existing ontologies have limitations in terms of information coverage, data heterogeneity and uncertainties consideration, and reusability across various application domains. This paper addresses these limitations by proposing uCSN, an ontology that builds upon and extends the Data Privacy Vocabulary (DPV), Semantic Sensor Network (SSN) and W3C Uncertainty ontologies, to provide a rich and expressive vocabulary for representing diverse user situations. We evaluate uCSN based on its consistency, accuracy, clarity and performance.

Authors

Institutions

Publication Details

Journal
Expert Systems
Published
2024-11-25
DOI
https://doi.org/10.1111/exsy.13792
Citations
1
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Ontology for In‐Depth Description of User Situations in Connected Environments

Richard Chbeir, Mahmoud Barhamgi, Philippe Arnould, Djamal Benslimane et al.
1 citations
Expert Systems
Context-Aware Activity Recognition Systems
0.25
article

An Ontology for In‐Depth Description of User Situations in Connected Environments

Richard Chbeir, Mahmoud Barhamgi, Philippe Arnould, Djamal Benslimane, Karam Bou‐Chaaya
article en
1 citations

Abstract

ABSTRACT Context‐awareness is increasingly recognised as a fundamental principle in the development of ubiquitous computing and ambient intelligence. By leveraging contextual data about users and their environments, systems can gain a deeper understanding of the evolving user situation. This empowers them to dynamically adapt their operations, leading to optimised resource utilisation, enhanced decision‐making, and ultimately, greater user satisfaction. However, a critical challenge lies in effectively representing user situations with a high degree of expressiveness. While ontology‐based data models have emerged as a promising approach due to their ability to handle the inherent heterogeneity of context information, existing ontologies have limitations in terms of information coverage, data heterogeneity and uncertainties consideration, and reusability across various application domains. This paper addresses these limitations by proposing uCSN, an ontology that builds upon and extends the Data Privacy Vocabulary (DPV), Semantic Sensor Network (SSN) and W3C Uncertainty ontologies, to provide a rich and expressive vocabulary for representing diverse user situations. We evaluate uCSN based on its consistency, accuracy, clarity and performance.

Expert SystemsVol. 42(2)
Université Claude Bernard Lyon 1 (FR), Université de Pau et des Pays de l'Adour (FR), Laboratoire d'Informatique en Images et Systèmes d'Information (FR), Qatar University (QA)
Qatar University
Openalex Percentile: Top 44%
Context-Aware Activity Recognition Systems
0.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

An Ontology for In‐Depth Description of User Situations in Connected Environments — Richard Chbeir, Mahmoud Barhamgi, et al. · Expert Systems (2024) | TGRS Research Map | TGRS