Managing digital libraries using semantic web technologies

The aim of this work is to improve retrieval and navigation services on bibliographic data held in digital libraries. This paper presents the design and implementation of OntoBib¸ an ontology-based bibliographic database system that adopts ontology-driven search in its retrieval. The presented work exemplifies how a digital library of bibliographic data can be managed using Semantic Web technologies and how utilizing the domain specific knowledge improves both search efficiency and navigation of web information and document retrieval.

Semantic and conceptual structures in module design

This paper describes the employment of semantic and conceptual structures in module design, specifically course modules. Additionally, it suggests other uses of these structures in aiding teaching and learning.

Personalised e-learning, semantic web and learning ontologies

Kurzel(2004) points out that researchers in e-learning and educational technologists, in a quest to provide improved Learning Environments (LE) for students are focusing on personalising the experience through a Learning Management System (LMS) that attempts to tailor the LE to the individual (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez,2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman; 2003). According to Kurzel (2004) this tailoring can have an impact on content and how it’s accessed; the media forms used; method of instruction employed and the learning styles supported. This project is aiming to move personalisation forward to the next generation, by tackling the issue of Personalised e-Learning platforms as pre-requisites for building and generating individualised learning solutions. The proposed development is to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system’s processing engine. This paper will discuss some of our early work and ideas.

Semantic web and e-learning

With emergence of "Semantic Web" there has been much discussion about the impact of technologies such as XML and RDF on the way we use the Web for developing e-learning applications and perhaps more importantly on how we can personalise these applications. Personalisation of e-learning is viewed by many authors (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez, 2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman, 2003) as the key challenge for the learning technologists. According to Kurzel (2004) the tailoring of e-learning applications can have an impact on content and how it's accesses; the media forms used; method of instruction employed and the learning styles supported. This paper will report on a research project currently underway at the eCentre in University of Greenwich which is exploring different approaches and methodologies to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system's processing engine.

Implementation of cognitive mapping, spatial representation and wayfinding behaviours of people within evacuation modelling tools

Within the building evacuation context, wayfinding describes the process in which an individual located within an arbitrarily complex enclosure attempts to find a path which leads them to relative safety, usually the exterior of the enclosure. Within most evacuation modelling tools, wayfinding is completely ignored; agents are either assigned the shortest distance path or use a potential field to find the shortest path to the exits. In this paper a novel wayfinding technique that attempts to represent the manner in which people wayfind within structures is introduced and demonstrated through two examples. The first step is to encode the spatial information of the enclosure in terms of a graph. The second step is to apply search algorithms to the graph to find possible routes to the destination and assign a cost to the routes based on their personal route preferences such as "least time" or "least distance" or a combination of criteria. The third step is the route execution and refinement. In this step, the agent moves along the chosen route and reassesses the route at regular intervals and may decide to take an alternative path if the agent determines that an alternate route is more favourable e.g. initial path is highly congested or is blocked due to fire.