Volume : 3, Issue : 1, January - 2014

Data Mining For XML Query–Answering Support

Prof. Dr. V. D. Kulkarni, Bansi Kaneria, Sayali Kothawade, Tunoor Rao, Vishakha Agrawal

Abstract :

Extracting information from semi structured documents is a very difficult task, and is going to become more and more critical as the amount of digital information available on the net grows. Documents are often so large that the data set returned as answer to a query may be too big to convey interpretable knowledge. In this paper, we describe an approach based on Tree-Based Association Rules (TARs): mined rules, which provide approximate, intentional information on both the structure and the contents of Extensible Markup Language (XML) documents, and can be stored in XML format as well. This mined knowledge is later used to provide: 1) a concise idea and the gist of both the structure and the content of the XML document 2) quick, approximate answers to queries. In this paper, we focus on the second feature. In recent years, the database research field has concentrated on the Extensible Markup Language (XML) as a flexible hierarchical model suitable to represent huge amounts of data with no absolute and fixed schema, and a possibly irregular and incomplete structure. There are two main approaches to XML document access: keyword-based search and query-answering. The first one comes from the convention of information retrieval , where most searches are performed on the textual content of the document; this means that no advantage is derived from the semantics conveyed b content of the document; this means that no advantage is derived from the semantics conveyed by the document structure.