Further, we will introduce some promising solutions for finding museums' Web sites and searching within them, such as Peer-to-Peer (P2P) search tools, intelligent agents that are specialized for information retrieval using "clustering" strategies based on user preferences, as well as systems of collaborative filtering and collaborative categorization from user experiences and judgments, allowing a personalized search. We will discuss some practical issues on finding museum information and how museums could improve their search rankings and their own searching facilities, especially with respect to the widely used Google search engine. We will show that many museum Web sites and databases belong to the hidden part of the Web that is not accessible unless the precise URL is known. The aim of this paper is to show the result of testing the difficulty of access to useful museum-related resources that are not very well known, but contain relevant information when search engines and other query techniques are used.
(This is once again the same in museums.) The role of search engines is particularly problematic because they have to guarantee the efficiency of the service without betraying the multiplicity of users' purposes. The Web is a unique "museum" where there are all sorts of data, including multimedia objects, so it is particularly crucial to respect the potentially different attitudes of users that may have various interests and different perspectives. Just as in a real museum, where maps and brochures help people organize their visit, search engines support the user in their navigation on the Web by assisting them in finding the most relevant material, while ignoring the rest. This is the same in museums there are some tools available to help people select what they want to see such as maps, brochures, etc. We can consider Web browsing as a similar experience to visiting a museum, when we take decisions about where to stop and where to skip over the exhibited objects.
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