Information extraction for semi-structured documents

The number of unstructured or semi-structured documents produced in all types of organizations continues to increase rapidly. Cost-effective ways of finding the relevant ones and extracting useful information from them are increasingly important to a large number of enterprises for operational and decision-support applications. The approach discussed in this paper constitutes a suitable basis for building an effective solution to extracting information from semi-structured documents for two principal reasons. First, it provides an extensible architecture basis for: extracting structured information from semistructured documents; providing fast and accurate selective access to this information; performing selective dissemination of relevant documents depending on filtering criteria. Second, it is simple in terms of: the complexity of the algorithms used for structure recognition and document filtering; the number and size of data structures required to perform the three functions mentioned above; the amount and complexity of the metadata required to handle a given collection of documents. The work described here is part of the Dyade Médiation project, which aims to provide integrated software components for accessing heterogeneous data sources in Internet/Intranet environments.