Searching the web by constrained spreading activation

Intelligent Information Retrieval is concerned with the application of intelligent techniques, like for example semantic networks, neural networks and inference nets to Information Retrieval. This field of research has seen a number of applications of Constrained Spreading Activation (CSA) techniques on domain knowledge networks. However, there has never been any application of these techniques to the World Wide Web. The Web is a very important information resource, but users find that looking for a relevant piece of information in the Web can be like 'looking for a needle in a haystack'. We were therefore motivated to design and develop a prototype system, WebSCSA (Web Search by CSA), that applied a CSA technique to retrieve information from the Web using an ostensive approach to querying similar to query-by-example. In this paper we describe the system and its underlying model. Furthermore, we report on an experiment carried out with human subjects to evaluate the effectiveness of WebSCSA. We tested whether WebSCSA improves retrieval of relevant information on top of Web search engines results and how well WebSCSA serves as an agent browser for the user. The results of the experiments are promising, and show that there is much potential for further research on the use of CSA techniques to search the Web. (C) 2000 Elsevier Science Ltd. All rights reserved.