Using Windmill Expansion for Document Retrieval

SEMIOTIKS aims to utilise online information to support the crucial decision–making of those military and civilian agencies involved in the humanitarian removal of landmines in areas of conflict throughout the world. An analysis of the type of information required for such a task has given rise to four main areas of research: information retrieval, document annotation, summarisation and visualisation. The first stage of the research has focused on information retrieval, and a new algorithm, “Windmill Expansion” (WE) has been proposed to do this. The algorithm uses retrieval feedback techniques for automated query expansion in order to improve the effectiveness of information retrieval. WE is based on the extraction of human–generated written phases for automated query expansion. Top and Second Level expansion terms have been generated and their usefulness evaluated. The evaluation has concentrated on measuring the degree of overlap between the retrieved URLs. The less the overlap, the more useful the information provided. The Top Level expansion terms were found to provide 90% of useful URLs, and the Second Level 83% of useful URLs. Although there was a decline of useful URLs from the Top Level to the Second Level, the quantity of relevant information retrieved has increased. The originality of SEMIOTIKS lies in its use of the WE algorithm to help non–domain specific experts automatically explore domain words for relevant and precise information retrieval

The Use of MOOCs to Change the Teaching of Literature Retrieval

The limits of the classic literature retrieval course teaching approach are becoming more obvious as information and communication technology advances in the digital age. The emergence of MOOCs (Massive Open Online Courses) has a significant influence on conventional literature retrieval course instruction, since it not only pushes but also gives the tools and techniques for improvement. The MOOCs literature retrieval course has transformed the teaching idea of the literature retrieval course. The evaluation mechanism is based on the construction of flipping the classroom lecture method of the instructors and students\u27 mutual assessment, which is one of the feasible solutions for the teaching reform of literary retrieval course

An Online Framework for Supporting the Evaluation of Personalised Information Retrieval Systems

Scope - Personalised Information Retrieval (PIR) has been gaining attention because it investigates intelligent ways for enhancing content delivery. Web users can have personalised services and more accurate information. Problem - Several PIR systems have been proposed in the literature; however, they have not been properly tested or evaluated. Proposal – The authors propose a generally applicable web-based interface, which provides PIR developers and evaluators with: i) implicit recommendations on how to evaluate a specific PIR system; ii) a repository containing studies on user-centred and layered evaluation studies; iii) recommendations on how to best combine different evaluation methods, metrics and measurement criteria in order to most effectively evaluate their system; iv) a UCE methodology which details how to apply existing UCE techniques; v) a taxonomy of evaluations of adaptive systems; and vi) interface translation support (49 languages supported)