Martin Dzbor

Magpie: supporting browsing and navigation on the semantic web

We describe several advanced functionalities of Magpie -- a tool that assists users with interpreting the web resources. Magpie is an extension to the Internet Explorer that automatically creates a semantic layer for web pages using a user-selected ontology. Semantic layers are annotations of a web page, with a set of applicable semantic services attached to the annotated items. We argue that the ability to generate different semantic layers for a web resource is vital to support the interpretation of web pages. Moreover, the assignment of semantic web services to the entities allows users to browse their neighbourhood semantically. At the same time, the Magpie suite offers trigger functionality based on the patterns of an automatically updated semantic log. The benefits of such an approach are illustrated by a semantically enriched browsing history management.

Collaborative Semantic Web Browsing with Magpie

Web browsing is often a collaborative activity. Users involved in a joint information gathering exercise will wish to share knowledge about the web pages visited and the contents found. Magpie is a suite of tools supporting the interpretation of web pages and semantically enriched web browsing. By automatically associating an ontology-based semantic layer to web resources, Magpie allows relevant services to be invoked as well as remotely triggered within a standard web browser. In this paper we describe how Magpie trigger services can provide semantic support to collaborative browsing activities.

Magpie: Experiences in supporting Semantic Web browsing

Magpie has been one of the first truly effective approaches to bringing semantics into the web browsing experience. The key innovation brought by Magpie was the replacement of a manual annotation process by an automatically associated ontology-based semantic layer over web resources, which ensured added value at no cost for the user. Magpie also differs from older open hypermedia systems: its associations between entities in a web page and semantic concepts from an ontology enable link typing and subsequent interpretation of the resource. The semantic layer in Magpie also facilitates locating semantic services and making them available to the user, so that they can be manually activated by a user or opportunistically triggered when appropriate patterns are encountered during browsing. In this paper we track the evolution of Magpie as a technology for developing open and flexible Semantic Web applications. Magpie emerged from our research into user-accessible Semantic Web, and we use this viewpoint to assess the role of tools like Magpie in making semantic content useful for ordinary users. We see such tools as crucial in bootstrapping the Semantic Web through the automation of the knowledge generation process.

Semantic Layering with Magpie

Web browsing involves two tasks: finding the right web page and then making sense of its content. So far, research has focused on supporting the task of finding web resources through ‘standard’ information retrieval mechanisms, or semantics-enhanced search. Much less attention has been paid to the second problem. In this paper we describe Magpie, a tool which supports the interpretation of web pages. Magpie offers complementary knowledge sources, which a reader can call upon to quickly gain access to any background knowledge relevant to a web resource. Magpie automatically associates an ontology-based semantic layer to web resources, allowing relevant services to be invoked within a standard web browser. From this perspective, Magpie may be seen as a step towards a semantic web browser The functionality of Magpie is illustrated using examples of how it has been integrated with our lab’s web resources.

From buddyspace to CitiTag: large-scale symbolic presence for community building and spontaneous play

In this paper we discuss the conceptual framework and principles that guide our work in the design of large-scale informal environments for collaborative work, learning and play, aiming to foster social bonds and to provide an exciting testbed for emergent social behaviours. We present three different applications we have developed: Buddyspace, an Instant Messaging environment for community building, BumperCars, an online presence-based multiplayer game and CitiTag, an experimental wireless mixed reality game.

Knowledge management in a distributed organisation

It has become very important for advanced organisations to make the best use of information gathered from various document sources inside companies and from external sources like the Internet. There are many technologies under development, which address knowledge discovery. On the other hand, there is a lack of efficient technologies focused on organising and sharing of existing knowledge. In this paper we introduce the research in scope of Know Webi (EC funded project). We focus our attention on two important issues — (i) how to capture tacit, contextual knowledge that is connected to the documents and (ii) how to support knowledge management in geographically distributed organisations through up-to-date communication and AI technologies

Representations for semantic learning webs : Semantic Web technology in learning support

Recent work on applying semantic technologies to learning has concentrated on providing novel means of accessing and making use of learning objects. However, this is unnecessarily limiting: semantic technologies will make it possible to develop a range of educational Semantic Web services, such as interpretation, structure-visualization, support for argumentation, novel forms of content customization, novel mechanisms for aggregating learning material, citation services and so on. In this paper, we outline an initial framework that extends the use of semantic technologies as a means of providing learning services that are owned and created by learning communities.

Semantic browsing with PowerMagpie

PowerMagpie is a tool that brings semantic interpretation to classical web pages by dynamically--i.e. during browsing--selecting and making use of a wide range of online available ontologies. We introduce the idea of extending browsing through semantic, ontology-based interpretation. Then, we provide a brief description of the architecture. In the end we underline which aspects of the available online semantic data are demonstrated, what the user may learn and which are the future directions.

Browsing for information by highlighting automatically generated annotations: a user study and evaluation

The realization of the Semantic Web is constrained by a knowledge acquisition bottleneck, i.e. the problem of how to add RDF mark-up to the millions of ordinary web pages that already exist. Information Extraction (IE) has been proposed as a solution to the annotation bottleneck. In the task based evaluation reported here, we compared the performance of users without access to annotation, users working with annotations which had been produced from manually constructed knowledge bases, and users working with annotations augmented using IE. We looked at retrieval performance, overlap between retrieved items and the two sets of annotations, and usage of annotation options. Automatically generated annotations were found to add value to the browsing experience in the scenario investigated.

REASE: the repository for learning units about the semantic web

REASE, EASEs repository for Semantic Web learning units, is a unique repository containing a diverse set of learning resources, ranging from annotated slides to video recordings, and from one-hour tutorials to fully-fledged university courses, for both academic and industrial audiences. It aims to accommodate the heterogeneous requirements of different users trying to learn about the Semantic Web. This article provides an introductory description of REASE, and describes the lessons learnt while creating and designing the repository, based on a mixed-method evaluation involving actual REASE users, a clean-room evaluation, and usage statistics gained from a transaction log analysis.