![](/images/graphics-bg.png)
Semantic Integration of Sensor Knowledge on Artificial Internet of Things
Joint Authors
Huang, Yikun
Xue, Xingsi
Jiang, Chao
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-25
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data.
To implement the communications and co-operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome heterogeneity problem among different sensors, which requires the utilization of sensor ontology.
Sensor ontology formally models the knowledge on AIoT by defining the concepts, the properties describing a concept, and the relationships between two concepts.
Due to human’s subjectivity, a concept in different sensor ontologies could be defined with different terminologies and contexts, yielding the ontology heterogeneity problem.
Thus, before using these ontologies, it is necessary to integrate their knowledge by finding the correspondences between their concepts, i.e., the so-called ontology matching.
In this work, a novel sensor ontology matching framework is proposed, which aggregates three kinds of Concept Similarity Measures (CSMs) and an alignment extraction approach to determine the sensor ontology alignment.
To ensure the quality of the alignments, we further propose a compact Particle Swarm Optimization algorithm (cPSO) to optimize the aggregating weights for the CSMs and a threshold for filtering the alignment.
The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s conference track and two pairs of real sensor ontologies to test cPSO’s performance.
The experimental results show that the quality of the alignments obtained by cPSO statistically outperforms other state-of-the-art sensor ontology matching techniques.
American Psychological Association (APA)
Huang, Yikun& Xue, Xingsi& Jiang, Chao. 2020. Semantic Integration of Sensor Knowledge on Artificial Internet of Things. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1214565
Modern Language Association (MLA)
Huang, Yikun…[et al.]. Semantic Integration of Sensor Knowledge on Artificial Internet of Things. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1214565
American Medical Association (AMA)
Huang, Yikun& Xue, Xingsi& Jiang, Chao. Semantic Integration of Sensor Knowledge on Artificial Internet of Things. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1214565
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214565