Discovering, structuring and visualizing organizations from Linked Open Data

The Semantic Web is aiming at structuring the information available on the Web, so that the information can be processed by machines autonomously. In this context, one of the most important successful projects in the area of Semantic Web is known as Linked Open Data (LOD). Till now a variety of data sets has been made available using LOD paradigm. However, extracting useful information from LOD and presenting it to general web users is a leading issue faced by the Semantic Web. Researchers are focusing to design Semantic Web applications that can easily present information to the end users, while abstracting away the under-lying details of the Semantic Web technologies. The designing of such an interface between Semantic Web and its users involves the following issues: 1) discovery of potential resources from LOD, 2) structuring the discovered resources, and 3) presenting the structured information in an easily understandable way. In our previous work, a Concept Aggregation Framework was proposed to address these issues and its implementation was carried out in a system named CAF-SIAL. The system is up and running since 2008. However, the scope of CAF-SIAL is currently limited to only one type of a resource, i.e. person. Users are able to find structured information about persons from CAF-SIAL. However, in the present work, we extend our system to include yet another valuable resource i.e. “organization”. We have selected fourteen different types of organizations like Airlines, Military and Universities etc. Each type of organization has number of properties. Importance of inclusion of the new resource and working of the system has been illustrated in a case study. The proposed extension will soon be incorporated in the running system.