LOV4IoT: A Second Life for Ontology-Based Domain Knowledge to Build Semantic Web of Things Applications

Abstract

Semantic Web of Things is a new field combining Semantic Web and Internet of Things technologies to be surrounded by smart objects and applications connected to the Web. On one hand, one of the Linked Open Data applications, called DataHub aims at referencing datasets, on the other hand, the Linked Open Vocabularies (LOV) references more than 400 ontologies. However, we discovered that more than 200 ontology-based projects relevant for IoT are not referenced on such tools since domain experts are not aware of them nor of the semantic web best practices. We propose the Machine-to-Machine Measurement (M3) framework, available online, to rapidly design and develop semantic-based cross-domain IoT applications by reusing as much as possible the domain knowledge (ontologies, datasets and rules). To achieve this goal, there are challenging steps: (1) referencing and classifying semantic-based projects relevant for IoT, (2) re-engineering a dataset of interoperable domain rules to deduce high-level abstractions from sensor data, (3) re-engineering an interoperable cross-domain knowledge to combine domains, and (4) assisting developers in designing IoT applications by designing pre-defined templates. In this article, we are focused on referencing and classifying semantic-based projects relevant for IoT by designing the Linked Open Vocabularies for Internet of Things (LOV4IoT) dataset. We also design a dataset of interoperable domain rules to deduce high-level abstractions from sensor data by designing the Sensor-based Linked Open Rules (S-LOR). This work has been applied to two uses cases: (1) redesigning a security and cross-domain knowledge base to assist users in suggesting security mechanisms to secure their applications, and (2) designing semantic-based IoT applications embedded in Android-powered devices.