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Semantics-based Dynamic Hypermedia Adaptation using the Hidden Markov Model

Information collection, selection, structuring, and presentation design are the core considerations for general hypermedia presentation generation systems. The content collection process can be enhanced by retrieving semantically related information objects, relevant to the topic selected by an author. Once relevant information objects are available, the content selection process suggests semantically related resources for the author's selection based on data usage history. The information objects are represented by media assets and descriptive documents. The semantic web technology that allows resource interoperability can be used for content description and interpretation of these information objects. By utilizing a semi-automatic approach, authors can be assisted at different stages of the presentation generation process. In this research, adaptation constraints are established independent of the author's proficiency (i.e. novice, intermediate or expert) by applying the Hidden Markov Model methodology. Semantically related media objects are suggested to authors for selection based on their interactive behaviour and the strength of semantic relations. The paper describes an application of the Hidden Markov Model in the initial authoring phases of semi-automatic hypermedia presentation systems.