We propose a multi-agent adaptive system to support tours of virtual museums. The system stores userspsila personalized views in zz-structures, particular data structures capable of representing both hypertext information and contextual interconnections among different information. We present an extension of the standard zz-structure model in terms of computational agents. These agents cooperate and collaborate in order to help users visualizing their personalized views. The power of this new model resides in the (partially limited) level of freedom users have for the dynamical choice, based on some present interest or necessity, of their navigational path inside the virtual museum.

Semantic linking has always been a strand of hypermedia research and is becoming central to current attempts to facilitate access to information in large hypertexts and the emerging 'semantic web' [Berners-Lee 1998a]. Due to the scaling problems with explicitly authored links between information items, it is likely that future large scale hypertexts will employ a mixture of authored links and indirect, computed links via some form of indexing system. Problems of information access are heightened by the lack of precision of current WWW retrieval technology and users unfamiliar with indexing conventions. There is a critical need for tools that will assist users to formulate and refine queries, and navigate through information spaces. Recent years have seen the growth of metadata, Digital Libraries, and interest in the application of traditional information science and library cataloguing techniques to the new environment of hypertext and the WWW. Semantic indexing provides a bridge between the various information disciplines. With the growing influence of the Resource Description Framework [Lassila 1999], semantic tagging and cataloguing of information is likely to become a key component of the information architecture of intranet hypertexts and the WWW.

As the Web expands exponentially, the need to put some order to its content becomes apparent. Hypertext categorization, that is the automatic classification of web documents into predefined classes, came to elevate humans from that task. The extra information available in a hypertext document poses new challenges for automatic categorization. HTML tags and metadata provide rich information for hypertext categorization that is not available in traditional text classification. This paper looks at (i) what representation to use for documents and which extra information hidden in HTML pages to take into consideration to improve the classification task, and (ii) how to deal with the very high number of features of texts. A hypertext dataset and four well-known learning algorithms (Naive Bayes, K-nearest neighbor, support vector machines and C4.5) were used to exploit the enriched text representation along with feature reduction. The results showed that enhancing the basic text content with HTML page keywords, title and anchor links improved the accuracy of the classification algorithms.

Achieving tomorrow's high-performance organizations will involve massive changes throughout their capability infrastructures. The complexity of implementing these changes will be daunting, and deserves a strategic approach. Groupware will support important, special new knowledge capabilities in these infrastructures, and also can play a key role in an evolutionary strategy.