Abstract
The Web of Things (WoT) can help with knowledge discovery and interoperability issues in many Internet of Things (IoT) applications. This article focuses on semantic modeling of WoT and proposes a new approach called Decomposition for Ontology Matching (DOM) to discover relevant knowledge by exploring correlations between WoT data using decomposition strategies. The DOM technique adopts several decomposition techniques to order highly linked ontologies of WoT data into similar groups. The main idea is to decompose the instances of each ontology into similar groups and then match instances of similar groups instead of entire instances of two ontologies. Three main algorithms for decomposition have been developed. The first algorithm is based on radar scanning, which determines the distribution of distances between each instance and all other instances to determine the cluster centroid. The second algorithm is based on adaptive grid clustering, where it focuses on distribution information and the construction of spanning trees. The third algorithm is based on split index clustering, where instances are divided into groups of cells from which noise is removed during the merging process. Several studies were conducted with different ontology databases to illustrate the use of the DOM technique. The results show that DOM outperforms state-of-the-art ontology matching models in terms of computational cost while maintaining the quality of the matching. Moreover, these results demonstrate that DOM is capable of handling various large datasets in WoT contexts.
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Index Terms
- Fast and Accurate Framework for Ontology Matching in Web of Things
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