Schema discovery for semistructured data
Ke Wang, Huiqing Liu · 1997
To formulate a meaningful query on semistructured data, such on the Web, that matches some of the source's structure, we need first to discover something about how the information is represented in the source. This is referred to as schema discovery and was considered for a single object recently. In the case of multiple objects, the task of schema discovery is to identify typical structuring information of those objects as a whole. We motivate the schema discovery in this general setting and propose a framework and algorithm for it. We apply the framework to a real Web database, the Internet Movies Database, to discover typical schema of most voted movies. Introduction As the amount of data available on-line grows rapidly, we find that more and more of the data is semistructured. In the semistructured world, data has no absolute schema fixed in advance, and the structure of data may be irregular or incomplete. Semistructured data arise when the source does not impose a ...