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Related Experiment Video

Updated: May 7, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Ontology alignment architecture for semantic sensor Web integration.

Susel Fernandez1, Ivan Marsa-Maestre, Juan R Velasco

  • 1Department of Computing Engineering, University of Alcala, Superior Polytechnic School, University Campus, Alcalá de Henares 28805, Madrid, Spain. susel.fernandez@uah.es.

Sensors (Basel, Switzerland)
|September 21, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fuzzy logic system for ontology alignment in the Semantic Sensor Web. It effectively integrates heterogeneous sensor data by improving concept mapping accuracy and recall.

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Last Updated: May 7, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Semantic Web Technologies

Background:

  • Sensor networks are crucial for data acquisition across diverse fields like industry and medicine.
  • The integration of Semantic Web technologies with sensor networks forms the Semantic Sensor Web, relying heavily on ontologies.
  • Semantic heterogeneity poses a significant challenge in integrating knowledge from diverse sensor network sources.

Purpose of the Study:

  • To develop a system for ontology alignment within the Semantic Sensor Web.
  • To address the challenge of semantic heterogeneity in sensor networks.
  • To improve the integration and exchange of knowledge from heterogeneous sensor data sources.

Main Methods:

  • The proposed system employs fuzzy logic techniques to combine similarity measures between entities from different ontologies.
  • It focuses on two key similarity measures: terminological similarity (linguistic and semantic context of entity names) and structural similarity (internal and relational concept structure).
  • The approach was validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) benchmark tests.

Main Results:

  • The fuzzy logic-based ontology alignment system demonstrated superior performance compared to previous methods.
  • The system achieved significant improvements in both precision and recall for ontology alignment tasks.
  • Validation using OAEI tests confirmed the effectiveness of the proposed terminological and structural similarity measures.

Conclusions:

  • The developed system offers an effective solution for ontology alignment in the Semantic Sensor Web.
  • Fuzzy logic techniques provide a robust mechanism for handling semantic heterogeneity in sensor networks.
  • The approach enhances knowledge integration and data exchange capabilities within the Semantic Sensor Web.