Use this URL to cite or link to this record in EThOS:
Title: Web site link prediction and semantic relatedness of web pages
Author: Jayalal, S. G. V. S.
Awarding Body: University of Keele
Current Institution: Keele University
Date of Award: 2005
Availability of Full Text:
Access through EThOS:
Relying solely on Web browsers to navigate large Web sites has created some navigation problems for users. Many researchers have stressed the importance of improving site user orientation and have suggested the use of information visualisation techniques, in particular "site maps" or "overview diagrams" to address this issue. Link prediction and the semantic relatedness of Web pages have been incorporated into such site maps. This thesis addresses disorientation within Web sites by presenting a visualisation of the site in order to answer one of the three fundamental questions identified by Nielsen and others that users might ask when they become disoriented while navigating a Web site, namely, Where am I now? Where have I been? Where can I go next? A method for making link predictions, which is based on Markov chains, has been developed and implemented in order to answer the third question, "where can I go next?". The method utilises information about the path already followed by the user. In addition to link prediction, pages which are semantically similar to the "current" page are automatically identified using an approach which is based on lexical chains. The proposed approach for link prediction using an exponentially-smoothed transition probability matrix incorporating site usage data over a time period was evaluated by comparing with similar approach developed by Sarukkai. The proposed semantic relatedness approach using weighted lexical chains was empirically compared with an earlier approach developed by Green using synset weight vectors. In conclusion, this thesis argues that Web site link prediction and the identification of semantically-related Web pages can be used to overcome disorientation. The approaches proposed are demonstrated to be superior to earlier methods.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available