User centred evaluation of an automatically constructed hyper-textbook

As hypertext systems become widely available and their popularity increases, attention has turned to converting existing textual documents into hypertextual form. An important issue in this area is the fully automatic production of hypertext for learning, teaching, training, or self-referencing. Although many studies have addressed the problem of producing hyper-books, either manually or semi-automatically, the actual usability of hyper-books tools is still an area of ongoing research. This article presents an effort to investigate the effectiveness of a hyper-textbook for self-referencing produced in a fully automatic way. The hyper-textbook is produced using the Hyper-TextBook methodology. We developed a taskbased evaluation scheme and performed a comparative usercentred evaluation between a hyper-textbook and a conventional, printed form of the same textbook. The results indicate that the hyper-textbook, in most cases, improves speed, accuracy, and user satisfaction in comparison to the printed form of the textbook

Modeling social information skills

In a modern economy, the most important resource consists in\ud human talent: competent, knowledgeable people. Locating the right person for\ud the task is often a prerequisite to complex problem-solving, and experienced\ud professionals possess the social skills required to find appropriate human\ud expertise. These skills can be reproduced more and more with specific\ud computer software, an approach defining the new field of social information\ud retrieval. We will analyze the social skills involved and show how to model\ud them on computer. Current methods will be described, notably information\ud retrieval techniques and social network theory. A generic architecture and its\ud functions will be outlined and compared with recent work. We will try in this\ud way to estimate the perspectives of this recent domain

Integrated content presentation for multilingual and multimedia information access

For multilingual and multimedia information retrieval from multiple potentially distributed collections generating the output in the form of standard ranked lists may often mean that a user has to explore the contents of many lists before finding sufficient relevant or linguistically accessible material to satisfy their information need. In some situations delivering an integrated multilingual multimedia presentation could enable the user to explore a topic allowing them to select from among a range of available content based on suitably chosen displayed metadata. A presentation of this type has similarities with the outputs of existing adaptive hypermedia systems. However, such systems are generated based on “closed” content with sophisticated user and domain models. Extending them to “open” domain information retrieval applications would raise many issues. We present an outline exploration of what will form a challenging new direction for research in multilingual information access

Conceptual Linking: Ontology-based Open Hypermedia

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of • an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; • a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents)

Conceptual Linking: Ontology-based Open Hypermedia

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of • an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; • a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents)

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements