Knowledge extraction using semantic similarity of concepts from Web of Things knowledge bases
Mahda Noura M.Sc.
Data & Knowledge Engineering
The Internet of Things (IoT) is one of the rapidly growing technologies with the aim of establishing communication among objects, people, and processes. This rapidly growing technology faces a lot of challenges that hinder its wider adoption, specifically in developing applications that involve heterogeneous domains. Currently, developing such interoperable applications require substantial efforts by the developers to hard code the requirements to ensure the correctness of transferring knowledge. The efforts can be significantly reduced by developing an interoperable platform that ensures seamless communication between heterogeneous IoT devices. W3C Web of Things (WoT) is a significant step towards enabling interoperability between IoT devices by integrating the existing Web ecosystem with ”Things”. WoT provides a unified interface over a suitable network protocol facilitating interactions between different IoT protocols. WoT Thing Descriptions (TD) enrich interoperability providing both human and machine readable metadata about a Thing. However, the WoT still falls short in providing semantic interoperability due to insufficient standard vocabularies which can describe different IoT application domains. In this paper, we propose a semantic similarity-based approach to automatically identify and extract the most common concepts from sixteen popular ontologies belonging to smart home and smart building domains. The proposed method helps the developers and researchers to develop a domain ontology with reduced effort. The extracted concepts are evaluated by the domain experts and are found to be sufficient in describing the smart home and smart building domains.
Muppavarapu, Vamsee; Ramesh, Gowtham; Gyrard, Amelie; Noura, Mahda: Knowledge extraction using semantic similarity of concepts from Web of Things knowledge bases. Data & Knowledge Engineering, pp. 101923, 2021.