Bayesian inference networks and spreading activation in hypertext systems

• Published in: Information processing & management Vol. 28; no. 3; pp. 389 - 406
• Main Author: Savoy, Jacques
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 1992; Elsevier Science; Pergamon Press; Elsevier Science Ltd
• Subjects: Bayesian analysis; Bayesian network; Bayesian Networks; Browsing; Exact sciences and technology; Full Text Databases; Hypermedia; Hypertext; Hypertext link semantics; Inference network; Information and communication sciences; Information processing and retrieval; Information Retrieval; Information retrieval in hypertext; Information retrieval systems. Information and document management system; Information science. Documentation; Information systems; Interfaces. Software; Mathematical models; Methods; Probabilistic inference; Prototypes; Sciences and techniques of general use; Search Strategies; Semantics; Spreading activation; Subject Index Terms; User Needs (Information); Hypertext; Information retrieval in hypertext; Information retrieval; Hypertext link semantics; Inference network; Bayesian network; Probabilistic inference; Spreading activation; Query; Semantic network; Browsing access; Information access; Activation; Tree structure; Statistical decision; Decision tree; Bayes decision; Automated processing
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/0306-4573(92)90082-B

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.