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Nowadays an enormous quantity of heterogeneous and distributed information is stored in the current digital libraries. Access to these collections poses a serious challenge, however, because present search techniques based on manually annotated metadata and linear replay of material selected by the user do not scale effectively or efficiently to large collections. The artificial intelligent and semantic Web provides a common framework that allows knowledge to be shared and reused. In this paper we propose a comprehensive approach for discovering information objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We suggest a conceptual architecture for a semantic and intelligent search engine. OntoFAMA is a collaborative effort that proposes a new form of interaction between people and Digital Library, where the latter is adapted to individuals and their surroundings. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from digital library of Seville University.

CHALLENGES IN DISTRIBUTED INFORMATION SEARCH IN A SEMANTIC DIGITAL LIBRARY