Abstrato

APPLICATION BASED SEMANTIC WEB MINING TECHNIQUE

Mahindra Pratap Singh Dohare and Sanjaydeep Singh Lodhi

Abstract-The Web is a huge read-write information space where many items such as documents, images or other multimedia can be accessed. In this context, several information technologies have been developed to help users to satisfy their searching needs on the Web, and the most used are search engines. Search engines allow users to find Web resources formulating queries (a set of terms) and reviewing a list of answers. The Semantic Web improves the Web infrastructure with formal semantics and interlinked data, enabling flexible, reusable, and open knowledge management systems. The move towards open and interlinked data on the Web and the Semantic Web results in more open systems. In contrast to traditional database-driven applications, open systems liberate the data that they operate on: sources are decentralized, data can be semistructured with arbitrary vocabulary and contributions can be published anywhere. This thesis offers algorithms and components that simplify and support knowledge management based on Semantic Web technology. We address four areas of Semantic Web application development: programmatic access: how to program against the flexible graph-based model; data navigation: how to navigate arbitrary information spaces; data entry: how to guide users through collaborative recommendation; and data discovery: how to locate relevant data sources. Our hypothesis is that the issues of programmatic access, data navigation, data entry, and data discovery can be addressed, with acceptable results, through the sole introspection of instance data at runtime, without relying on fixed schema structures at design time. In all four areas we devise solutions that are domain independent, rely only on instance data and dynamically adjust to the available data.