By Gareth J. F. Jones (auth.), Andreas Nürnberger, Marcin Detyniecki (eds.)
This publication is a longer selection of contributions that wereoriginally subm- ted to the first overseas Workshop on Adaptive Multimedia Retrieval (AMR 2003), which was once equipped as a part of the twenty sixth German convention on Arti?cial Intelligence (KI 2003),and held in the course of September 15–18,2003at the collage of Hamburg, Germany. encouraged through the final luck of the workshop – as published by way of the stimulating surroundings through the workshop and the variety of very and energetic contributors – we ?nally made up our minds to edit a publication in accordance with revised papers that have been before everything submitted to the workshop. additionally, we invited a few extra introductory contributions so one can be capable to offer a conclusive booklet on present issues within the region of adaptive multimedia retrieval structures. we are hoping that we have been capable of prepare a stimulating number of articles for the reader. we love to thank the organizationcommittee of the twenty sixth German convention on Arti?cial Intelligence (KI 2003) for delivering the atmosphere and the admin- trative help in figuring out this workshop as a part of their software. specifically, we adore to thank Christopher Habel for selling the workshop as a part of the convention software and Andreas Gun ¨ ther for his variety help during the association process.
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Additional info for Adaptive Multimedia Retrieval: First International Workshop, AMR 2003, Hamburg, Germany, September 15-16, 2003, Revised Selected and Invited Papers
We have been exploring interfaces that can support users in scrutinising large user models. In this paper, we will describe two recent innovations in that work. The first makes use of an ontology to structure the user model into a graph whose structure should give flexible support for users scrutinising related parts of the user model. We describe the way that we construct this ontology in a manner that is consistent with our goal of scrutability. In the case of the ontology, we want users to be able scrutinise the structure of the ontology and the processes used to form it.
Eventually, content description may be fixed into a knowledge base. It is shown that such an approach tightly relating content-based retrieval and content description poses new solvable challenges, as opposed to classical CBIR whose performances tend to saturate in current systems. Fig. 2. The functional schema of a CBIR system completed with possible acquisition of semantic knowledge. K, RJ and U mark places where a priori Knowledge, Relevance Judgments and User interaction may be inserted, respectively In the setup shown in Figure 2, user knowledge is captured at various locations of an integrated framework.
Importantly, we describe an extension that enables a person to create new metadata terms and link these elegantly into the ontology. We report a qualitative study in the use of the Metasaur interface for its two roles, creation of metadata and scrutiny of the user modelling processes. 1 Introduction The holy grail of improved e-learning is the possibility of providing each learner with their own, personalised tutor. Evidence from studies of human one-to-one tutoring  indicate the potential for huge gains in learning effectiveness, potentially taking the student at the average level in the class up to the top of the class’s level of performance.