ONTOLOGY-BASED CONTENT SIMILARITY MEASURE FOR IOT INFORMATION RETRIEVAL
Abdulrahman Jalal Erhaim · 2018
Interne of things (IoT) becomes a trending technology topic due to the power it delivers in sensing the data in regard to our daily aspects of various environments, and by 2020 assuming there will be over 26 billion connected (IoT) sensors and devices around the world. In order to use the data that comes from this tremendous amount of sensors and devices, we should have a system that helps the user in retrieving the desired information out of the billions of stored data from the sensors and devices. There had been some works to help in retrieving the desired information from the massive data storage by using SPARQL query language, but it stills inefficient in the regard to the precision of the retrieved information from the massive data storage since it only uses relaxation procedure. Hence, in this thesis document, we present our SPARQL querying language enhanced system which helps in retrieving the desired information from the massive data storage with the highest precision by using semantic correction and relaxation procedures in order to satisfy the user querying. Our procedures are applied to semantic information retrieval systems with using OWL and RDF ontologies that are related to (IoT) applications. To prove the efficiency of using our semantic correction and relaxation procedures, we have developed a SPARQL querying tools to evaluate the result of our procedures. In the end, for the user querying information retrieval, our semantic correction and relaxation procedures perform a higher percent of precision than the others related work procedures which based only on relaxations.