Large collections of documents containing various types of multimedia, are made available to the WWW. Unfortunately, due to the un-structuredness of Internet environments it is hard to find specific information when one is looking for it. Search engines available can only rely their results on information retrieval techniques and most of the time they lack the desired power in query formulation. Modelling data on the web, as if it was designed for use within databases, should provide us with the necessary basis for enhancing this query formulation. This of course requires special care for dealing with the included multimedia data and the semi-structured aspects of data on the web. Modelling the entire web would be too ambitious, therefore we focus on a more feasible environment, like the intranet, where one can find large collections of related data. With the webspace method we have already shown how to deal with the various aspects of semi-structured data in large collections of related documents. In this paper we focus on the integration of our webspace method for concept-based search with content-based multiq9 i information retrieval (IR). A webspace consi9 s of two levels. At the document level, a webspace is con ered to be a collection of related documents. At the semantical level, concepts are defined to be used i the documents at the document level. By modelling these concepts using a webspace schema a semantinq level of abstraction is gained. This supplies the necessary platform for uery i g data avai9H le within a specific webspace. For the itegratia with content-based informat iq retriN al an existing IR model is adopted. We will discuss how this is used in the context of Mirror, a Multi media DBMS, and how this fram...

In this report the development of a specialised search engine for a digital library is described. The proposed system architecture consists of three levels: the conceptual, the logical and the physical level. The conceptual level schema enables by its exposure of a domain specific schema semantically rich conceptual search. The logical level provides a description language to achieve a high degree of flexibility for multimedia retrieval. The physical level takes care of scalable and e#cient persistent data storage. The role, played by each level, changes during the various stages of a search engine's lifecycle: (1) modeling the index, (2) populating and maintaining the index and (3) querying the index. The integration of all this functionality allows the combination of both conceptual and content-based querying in the query stage. A search engine for the Australian Open tennis tournament website is used as a running example, which shows the power of the complete architecture and its various components.