Data Mining For Intensional Query Answering Using Tree Based Association Rules
The database research field has concentrated on the Extensible Markup Language (XML) due to its flexible hierarchical nature which can use to represent huge amounts of data, also it doesn't have absolute and fixed schema, but having possibly irregular and incomplete structure. Extracting information from semi structured documents is a very hard task, and is going to become more and more critical as the amount of digital information available on the internet grows. Indeed, documents are often so large that the dataset returned as answer to a query may be too big to convey interpretable knowledge. Since query languages for semi structured data rely the one document structure to convey its semantics, in order for query formulation to be effective users need to know this structure in advance, which is often not the case. When users specify queries without knowing the document structure, they may fail to retrieve information which was there, but under a different structure. An approach based on TreeBased Association Rules (TARs) which provide approximate, intentional information about the structure and the contents of XML documents both, as well as it can be stored in XML format. This mined knowledge is used to provide, a concise idea of both the structure and the content of the XML document and quick, approximate answers to queries whenever required. KeywordsExtensible markup Language (XML) approximate query answering, data mining, intentional Information, Tree Based Association Rules (TARs)