BAYESIAN-INFERENCE NETWORKS AND SPREADING ACTIVATION IN HYPERTEXT SYSTEMS

Browsing is the foremost method in searching through information in a hypertext or hypermedia system. However, as the number of nodes and links increases, this technique is far from satisfactory, and other search mechanisms must be provided. Classical search techniques such as menu selection hierarchies, string matching, Boolean query, etc., are already available, but they treat nodes as independent entities rather than considering the link semantics between nodes. Moreover, in order to write a query the users often encounter many problems such as how to find the appropriate terms that describe the information needs, how to correctly write a query in a language using artificial syntax, etc. This paper describes an alternative based on a Bayesian network that structures the indexing terms and stores the user's information needs. In our approach, the user does not have to write a formal query because the computation required is accomplished automatically and without any prior information or constraint. Moreover, using a constrained spreading activation, our solution uses link semantics to search relevant starting points for browsing.