Light-weight ontologies for scrutable user modelling

This thesis is concerned with the ways light-weight ontologies can support scrutability for large user models and the user modelling process. It explores the role that light-weight ontologies can play, and how they can be exploited, for the purpose of creating and maintaining large, scrutable user models consisting of hundreds of components. We address problems in four key areas: ontology creation, metadata annotation, creation and maintenance of large user models, and user model visualisation, with a goal to provide a simple and adaptable approach that maintains scrutability. Each of these key areas presents a number of challenges that we address. Our solution is the development of a toolkit, LOSUM, which consists of a number of tools to support the user modelling process. It incorporates light-weight ontologies to fulfill a number of roles: aiding in metadata creation, providing structure for large user model visualisation, and as a means to reason across granularities in the user model. In conjunction with this, LOSUM also features a novel visualisation tool, SIV, which performs a dual role of ontology and user model visualisation, supporting the process of ontology creation, metadata annotation, and user model visualisation. We evaluated our approach at each stage with small user studies, and conducted a large scale integrative evaluation of these approaches together in an authentic learning context with 114 students, of whom 77 had exposure to their learner models through SIV. The results showed that students could use the interface and understand the process of user model construction. The flexibility and adaptability of the toolkit has also been demonstrated in its deployment in several other application areas

Visualisation of ontological inferences for user control of personal web agents

work is in the area of scrutable user models, ontologies and the visualisation of ontologies and personal data. Title of Pape

Ontology-Based Open-Corpus Personalization for E-Learning

Conventional closed-corpus adaptive information systems control limited sets of documents in predefined domains and cannot provide access to the external content. Such restrictions contradict the requirements of today, when most of the information systems are implemented in the open document space of the World Wide Web and are expected to operate on the open-corpus content. In order to provide personalized access to open-corpus documents, an adaptive system should be able to maintain modeling of new documents in terms of domain knowledge automatically and dynamically. This dissertation explores the problem of open-corpus personalization and semantic modeling of open-corpus content in the context of e-Learning. Information on the World Wide Web is not without structure. Many collections of online instructional material (tutorials, electronic books, digital libraries, etc.) have been provided with implicit knowledge models encoded in form of tables of content, indexes, headers of chapters, links between pages, and different styles of text fragments. The main dissertation approach tries to leverage this layer of hidden semantics by extracting and representing it as coarse-grained models of content collections. A central domain ontology is used to maintain overlay modeling of students’ knowledge and serves as a reference point for multiple collections of external instructional material. In order to establish the link between the ontology and the open-corpus content models a special ontology mapping algorithm has been developed. The proposed approach has been applied in the Ontology-based Open-corpus Personalization Service that recommends and adaptively annotates online reading material. The domain of Java programming has been chosen for the proof-of-concept implementation. A controlled experiment has been organized to evaluate the developed adaptive system and the proposed approach overall. The results of the evaluation have demonstrated several significant learning effects of the implemented open-corpus personalization. The analysis of log-based data has also shown that the open-corpus version of the system is capable of providing personalization of similar quality to the close-corpus one. Such results indicate that the proposed approach successfully supports open-corpus personalization for e-Learning. Further research is required to verify if the approach remains effective in other subject domains and with other types of instructional content

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions