Artificial Neural Network Based Sensor Ontology Matching Technique

Web of Things (WoT) is capable of promoting the knowledge discovery and address interoperability problems of various Internet of Things (IoT) applications. To integrate the semantic information on WoT, Semantic Sensor Networks (SSN) based knowledge engineering is utilized for the uniform representation of identical knowledge by using the sensor ontology. However, it is arduous to link numerous heterogeneous sensor entities, and the Sensor Ontology Matching (SOM) is a newly emerging technique for solving the ontology heterogeneity problem, which aims at finding the semantically identical sensor entities in two ontologies. In this paper, the problem of SOM is deemed as regression problem that to integrate a number of Entity Similarity Measure (EMS) to estimate the real similarity score between two sensor entities. To address it, we propose a Artificial Neural Network (ANN)-based sensor ontology matching technique (ANN-OM), which employs the representative entities for enhancing the quality of alignment and the matching efficiency. The experimental results illustrate that ANN-OM is capable of determining superior alignment which is better than the state-of-the-art ontology matchers.