An Innovative of Semantics-Empowered Sensors, Services and Social Computing on the Ubiquitous Web
E. Fathima · 2014
Semantic Web architecture is the automated conversion and storage of unstructured data sources in a semantic web database. Semantic Web applications are intended to automatically extract and process the concepts and context in the database in a range of highly flexible tools. The Project presents a method for rapid development of benchmarks for Semantic Web knowledge base systems. At the core, we have a synthetic data generation approach for RDF that is scalable and models the real world data. The data-generation algorithm learns from real domain documents and generates benchmark data based on the extracted properties relevant for benchmarking. This is important because relative performance of systems will vary depending on the structure of the ontology and data used. The proposed system approach helps overcome the problem of having insufficient information on the structure of the ontology and data used for benchmarking and allows us to develop benchmarks for a variety of domains and applications in a very time efficient manner. The proposed system measure and analyze the graph features of Semantic Web (SW) schemas with focus on power law degree distributions and conducted an experiment of synthetic data generation on five Semantic Web knowledge base systems. The Project mainly aims to prove the influence of ontology and data on the capability and performance of the systems and thus the need of using a representative benchmark for the intended application of the systems.