Social Web in IoT
Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things
Bibliographic Data
| ID | 22108302 |
|---|---|
| Authors | Asma Belhadi (0000-0002-7103-2179, OsloMet – Oslo Metropolitan University), Djamel Djenouri (0000-0001-9333-9860, University of the West of England), Youcef Djenouri (0000-0003-0135-7450, University of South-Eastern Norway), Ahmed Nabil Belbachir (0000-0001-9233-3723, NORCE Research AS), Gautam Srivastava (0000-0001-9851-4103, China Medical University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 3 |
| Pages | 3966-3977 |
| Publication date | 2024-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3332562 |
| OpenAlex | W4389633707 |
| Language | EN |
| References cited | 66 |
Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we suggest a novel solution, evolutionary clustering for ontology matching (ECOM), to explore correlations between S-WoT data using clustering and evolutionary computation methodologies. The ECOM approach uses a variety of clustering techniques to aggregate S-WoT data's strongly related ontologies into comparable categories. The principle is to match concepts of similar groups rather than full concepts of two ontologies, which necessitates splitting examples of each ontology into similar groups. We design two clustering algorithms for ontology matching using conventional methods, as well as sophisticated clustering techniques. Moreover, we develop an intelligent matching algorithm that uses evolutionary computation to quickly converge to (or ideally identify) optimal matches. Numerous simulations have been conducted using various ontology databases to demonstrate the application and precision of ECOM. Our findings clearly show that ECOM has better results when compared to cutting-edge ontology matching methods. The F-measure of ECOM exceeds 95% whereas it does not reach 90% for all baseline methods. The results also confirm that ECOM scales with big data in S-WoT environments
Cluster analysis · Data mining · Data science · Information retrieval · Internet of Things · Interoperability · Ontology · Semantic Web · Web of Things · World Wide Web · Caching and Content Delivery · Computer Science · Digital Marketing and Social Media · Mathematics · Service-Oriented Architecture and Web Services · Artificial Intelligence · Theoretical Computer Science
| Highly cited | No |
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