PerfectO: An Online Toolkit for Improving Quality, Accessibility, and Classification of Domain-Based Ontologies

Sensor-based applications are increasingly present in our everyday life. Due to the enormous quantity of sensor data produced, interpreting data and building interoperable sensor-based applications is needed. There are several problems to address the heterogeneity of (1) data format, (2) languages to describe sensor metadata, (3) models for structuring sensor datasets, (4) reasoning mechanisms and rule languages to interpret sensor datasets, and (5) applications. Semantic Web technologies (a.k.a, knowledge graphs), are immersed in an increasing number of online activities we perform today (e.g., search engines for gathering information). There is a need to find better ways to share data and distribute more meaningful and more accurate information. Innovative methodologies are needed to link and associate the data from different domains to improve knowledge discovery. Semantic knowledge graphs, made of datasets and ontologies, are intended to describe and organize heterogeneous data explicitly. If an ontology is widely used to structure data of a particular domain, the accessibility and the efficiency in sharing and reusing that information will increase. For this reason, we focused on the ontology quality used when building sensor-based applications. We designed PerfectO, a Knowledge Directory Services tool, focusing on ontology best practices, which: (1) improves knowledge quality, (2) leverages usability, accessibility, and classification of the information, (3) enhances engineering experience, and (4) promotes engineering best practices. PerfectO implementation is applied to the Internet of Things (IoT) domain because it covers more than 20 application domains (e.g., healthcare, smart building, smart farm) that use sensors. PerfectO enhances knowledge expertise quality implemented within any ontologies as demonstrated with the Linked Open Vocabularies for IoT (LOV4IoT) ontology catalog.