Patrick Sinclair

Links in the palm of your hand: tangible hypermedia using augmented reality

Contextualised Open Hypermedia can be used to provide added value to document collections or artefacts. However, transferring the underlying hyper structures into a users conceptual model is often a problem. Augmented reality provides a mechanism for presenting these structures in a visual and tangible manner, translating the abstract action of combining contextual linkbases into physical gestures of real familiarity to users of the system. This paper examines the use of augmented reality in hypermedia and explores some possible modes of interaction that embody the functionality of open hypermedia and contextual linking using commonplace and easily understandable real world metaphors.

SCULPTEUR: Multimedia Retrieval for Museums

The paper describes the prototype design and development of a multimedia system for museums and galleries. Key elements in the system are the introduction of 3-D models of museum artefacts together with 3-D as well as 2-D content based retrieval and navigation facilities and the development of a semantic layer, centred on an ontology for museums, which aims to expose the richness of knowledge associated with the museum collections and facilitate concept based retrieval and navigation integrated with that based on content and metadata. Interoperability protocols are designed to allow external applications to access the collection and an example is given of an e-Learning facility which uses models extracted to a virtual museum.

Ontologies as facilitators for repurposing web documents

This paper investigates the role of ontologies as a central part of an architecture to repurpose existing material from the web. A prototype system called ArtEquAKT is presented, which combines information extraction, knowledge management and consolidation techniques and adaptive document generation. All of these components are co-ordinated using one central ontology, providing a common vocabulary for describing the information fragments as they are processed. Each of the components of the architecture is described in detail and an evaluation of the system discussed. Conclusions are drawn as to the effectiveness of such an approach and further challenges are outlined.

Augmented reality as an interface to adaptive hypermedia systems

Adaptive Hypermedia has sought to tackle the problems of dealing with complex, heavily structured information and the presentation of views of that structure to users. Increasingly, adaptive content is achieved through different forms of context. Using two case-study applications, we will reflect on how Augmented Reality may present solutions to a number of Adaptive Hypermedia presentation problems. Each case study describes a different physical interaction metaphor for exposing the complex adaptation of hypermedia content in an intuitive way. The preliminary findings of our early evaluations are discussed. Finally, conclusions are drawn as to how Augmented Reality applications could use the modelling techniques of the Adaptive Hypermedia community to deal more easily with complex information.

Challenges in supporting faceted semantic browsing of multimedia collections

This paper describes our experience developing a semantically-rich exploratory interface that integrates a suite of Newsfilm media sources and associated information. Indeed, the affordances for real time, direct manipulation queries provided by the UI have driven the engineering challenges to support such queries across the network. In this paper we review our efforts first to use strictly semantic web technologies, then hybrid database with semantic web wrapper technologies, and now, ultimately, standard database technologies for real time dynamic queries. We consider how the lessons learned about performance design to support such rich queries will need to be addressed in semantic web technologies in order to better support thsemantic exploratory experience across distributed data resources.

How to consume linked data on the web: tutorial description

In the past two years, the amount of data published in RDF and following the Linked Data principles has increased dramatically. Everyday people are publishing datasets as Linked Data. However, applications that consume Linked Data are not mainstream yet. To overcome this issue, we present a beginners tutorial on consuming Linked Data. We will discuss existing techniques how users can currently consume Linked Data and use it in their current applications.

Semantic web integration of cultural heritage sources

In this paper, we describe research into the use of ontologies to integrate access to cultural heritage and photographic archives. The use of the CIDOC CRM and CRM Core ontologies are described together with the metadata mapping methodology. A system integrating data from four content providers will be demonstrated.

Towards a Canonical View of Peer Assessment

Peer assessment (or peer review) is a popular form of reciprocal assessment where students produce feedback, or grades, for each others work. Peer assessment activities can be extremely varied with participants taking different roles at different stages of the process and materials passing between roles in sophisticated patterns. This variety makes designing peer assessment systems very challenging. In this paper we present a number of peer assessment case studies and show how a simple review cycle can be used as a building block to achieve the more complex cases. We then propose a canonical use case for peer assessment, in which a review plan is used to describe how review cycles can be combined to achieve the required complexity.

Dynamic link service 2.0: using wikipedia as a linkbase

This paper describes how a Web 2.0 mashup approach, reusing technologies and services freely available on the web, have enabled the development of a dynamic link service system that uses Wikipedia as its linkbase.

Giving order to image queries

Users of image retrieval systems often find it frustrating that the image they are looking for is not ranked near the top of the results they are presented. This paper presents a computational approach for ranking keyworded images in order of relevance to a given keyword. Our approach uses machine learning to attempt to learn what visual features within an image are most related to the keywords, and then provide ranking based on similarity to a visual aggregate. To evaluate the technique, a Web 2.0 application has been developed to obtain a corpus of user-generated ranking information for a given image collection that can be used to evaluate the performance of the ranking algorithm.