Intelligent Document Retrieval

Collections of digital documents can nowadays be found everywhere in institutions, universities or companies. Examples are Web sites or intranets. But searching them for information can still be painful. Searches often return either large numbers of matches or no suitable matches at all.

Such document collections can vary a lot in size and how much structure they carry. What they have in common is that they typically do have some structure and that they cover a limited range of topics. The second point is significantly different from the Web in general.

The type of search system that we propose in this book can suggest ways of refining or relaxing the query to assist a user in the search process. In order to suggest sensible query modifications we would need to know what the documents are about. Explicit knowledge about the document collection encoded in some electronic form is what we need. However, typically such knowledge is not available. So we construct it automatically.

Using markup structure alone to extract knowledge from documents is new Domain knowledge is extracted from documents in a fully automated process The techniques outlined avoid the bottleneck of manual customization Searching a document collection can be seen as navigating the user through the automatically extracted domain knowledge Combines the theoretical framework and detailed evaluation steps

Contenu
Related Work.- Data Analysis and Domain Model Construction.- Incorporating Additional Knowledge.- A Dialogue System for Partially Structured Data.- UKSearch - Intelligent Web Search.- UKSearch - Evaluation and Discussion.- YPA - Searching Classified Directories.- Future Directions and Conclusions.