Application of User Profiling on Ontology Module Extraction for Medical portals

One fit all for approach for searching and ranking discovered knowledge on the Internet does not cater for the diverse variety of users and user groups with different preferences, information needs and priorities. This is of a particular case in the National electronic Library of Infection in the UK (NeLI, www.neli.org.uk) accessed by a number of medical professionals with different preferences and medical information needs. We define personal and group profiles, based on user-specified interests, and develop an ontology module extraction service defining the key area of the infection ontology of a particular relevance to each user group. In this paper we discuss how ontology modularisation can improve the NeLI portal by providing customised alert, recommender service and specialitycustomised browsing tree structure

Comparison of implicit and explicit feedback from an online music recommendation service

Explicit and implicit feedback exhibits different characteristics of users ’ preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics

A user-centred evaluation framework for the Sealife semantic web browsers

BACKGROUND: Semantically-enriched browsing has enhanced the browsing experience by providing contextualized dynamically generated Web content, and quicker access to searched-for information. However, adoption of Semantic Web technologies is limited and user perception from the non-IT domain sceptical. Furthermore, little attention has been given to evaluating semantic browsers with real users to demonstrate the enhancements and obtain valuable feedback. The Sealife project investigates semantic browsing and its application to the life science domain. Sealife's main objective is to develop the notion of context-based information integration by extending three existing Semantic Web browsers (SWBs) to link the existing Web to the eScience infrastructure. METHODS: This paper describes a user-centred evaluation framework that was developed to evaluate the Sealife SWBs that elicited feedback on users' perceptions on ease of use and information findability. Three sources of data: i) web server logs; ii) user questionnaires; and iii) semi-structured interviews were analysed and comparisons made between each browser and a control system. RESULTS: It was found that the evaluation framework used successfully elicited users' perceptions of the three distinct SWBs. The results indicate that the browser with the most mature and polished interface was rated higher for usability, and semantic links were used by the users of all three browsers. CONCLUSION: Confirmation or contradiction of our original hypotheses with relation to SWBs is detailed along with observations of implementation issues

Semantic Web Applications and Tools for Life Sciences, 2008 – Introduction

BACKGROUND: Semantically-enriched browsing has enhanced the browsing experience by providing contextualized dynamically generated Web content, and quicker access to searched-for information. However, adoption of Semantic Web technologies is limited and user perception from the non-IT domain sceptical. Furthermore, little attention has been given to evaluating semantic browsers with real users to demonstrate the enhancements and obtain valuable feedback. The Sealife project investigates semantic browsing and its application to the life science domain. Sealife's main objective is to develop the notion of context-based information integration by extending three existing Semantic Web browsers (SWBs) to link the existing Web to the eScience infrastructure. METHODS: This paper describes a user-centred evaluation framework that was developed to evaluate the Sealife SWBs that elicited feedback on users' perceptions on ease of use and information findability. Three sources of data: i) web server logs; ii) user questionnaires; and iii) semi-structured interviews were analysed and comparisons made between each browser and a control system. RESULTS: It was found that the evaluation framework used successfully elicited users' perceptions of the three distinct SWBs. The results indicate that the browser with the most mature and polished interface was rated higher for usability, and semantic links were used by the users of all three browsers. CONCLUSION: Confirmation or contradiction of our original hypotheses with relation to SWBs is detailed along with observations of implementation issues

Who is behind the Model? Classifying Modelers based on Pragmatic Model Features

\u3cp\u3eProcess modeling tools typically aid end users in generic, non-personalized ways. However, it is well conceivable that different types of end users may profit from different types of modeling support. In this paper, we propose an approach based on machine learning that is able to classify modelers regarding their expertise while they are creating a process model. To do so, it takes into account pragmatic features of the model under development. The proposed approach is fully automatic, unobtrusive, tool independent, and based on objective measures. An evaluation based on two data sets resulted in a prediction performance of around 90%. Our results further show that all features can be efficiently calculated, which makes the approach applicable to online settings like adaptive modeling environments. In this way, this work contributes to improving the performance of process modelers.\u3c/p\u3

National electronic library of infection (NeLI/NRIC) - the best available evidence online: 9 years of experience

Substantial budgets have been spent recently on ehealth programmes by many governments aiming to change the way healthcare is delivered in the 21st century. This paper discusses the background and evaluation of the National electronic Library of Infection in the UK (NeLI, www.neli.org.uk) and its subsidiary project the National Resource for Infection Control (NRIC, www.nric.org.uk) in the UK. An evaluation study conducted on NRIC demonstrated the growing interest in the portal, widening user base, search needs and a knowledge change. As the site has a potential for a very cost-effective technology transfer, international versions for non-English speaking professionals, localise sections and collections for those from other domains than infection are being investigated

Evaluating engagement to address undergraduate first year transition: A case study

Rapidly changing demands from employers of students of business meant substantial redesign of the first year undergraduate experience whose underlying pedagogy drew on the concept of "high-engagement" learning. This paper focuses on the question of how engagement can be evaluated. It is argued that a variety of "sensors" are needed for evaluation, both quantitative and qualitative. Of particular interest is the use of Moodle logs as an emerging powerful sensor