Slicepedia: Providing Customized Reuse of Open-Web Resources for Adaptive Hypermedia

Abstract

A key advantage of Adaptive Hypermedia Systems(AHS) is their ability to re-sequence and reintegrate content to satisfy particular user needs. However, this can require large volumes of content, with appropriate granularities and suitable meta-data descriptions. This represents a major impediment to the mainstream adoption of Adaptive Hypermedia. Open Adaptive Hypermedia systems have addressed this challenge by leveraging open corpus content available on the World Wide Web. However, the full reuse potential of such content is yet to be leveraged. Open corpus content is today still mainly available as only one-size-fits-all document-level information objects. Automatically customizing and right-fitting open corpus content with the aim of improving its amenability to reuse would enable AHS to more effectively utilise these resources. This paper presents a novel architecture and service called Slicepedia, which processes open corpus resources for reuse within AHS. The aim of this service is to improve the reuse of open corpus content by right-fitting it to the specific content requirements of individual systems. Complementary techniques from Information Retrieval, Content Fragmentation, Information Extraction and Semantic Web are leveraged to convert the original resources into information objects called slices. The service has been applied in an authentic language elearning scenario to validate the quality of the slicing and reuse. A user trial, involving language learners, was also conducted. The evidence clearly shows that the reuse of open corpus content in AHS is improved by this approach, with minimal decrease in the quality of the original content harvested.