An Application of Collaborative Web Browsing Based on Ontology Learning from User Activities on the Web

With explosively increasing amount of information on the Web, users have been getting more bored to seek relevant information. Several studies have introduced adaptive approaches to recognizing personal interests. This paper proposes the collaborative Web browsing system that can support users to share knowledge with other users. Especially, we have focused on user interests extracted from their own activities related to bookmarks. A simple URL based bookmark is provided with semantic and structural information by the conceptualization based on ontology. In order to deal with the dynamic usage of bookmarks, ontology learning based on a hierarchical clustering method can be exploited. As a result of our experiments, about 53.1 % of the total time was saved during collaborative browsing for seeking the equivalent set of information, compared with single Web browsing. Finally, we demonstrate implementing an application of collaborative browsing system through sharing bookmark-associated activities

MoPark Initiative, Metadata Options Appraisal (Phase I)

Examines – and makes recommendations on - the needs of the Loch Lomond and Trossachs National Park as regards the metadata, metadata standards, and metadata management required for the competent handling of digital materials both now and in the future. Proposes an iterative approach to determining metadata requirements, working within a METS-based framework

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

Access to Adjudication Materials on Federal Agency Websites

This Article offers recommendations and best practices for federal administrative agencies interested in improving the accessibility of orders, opinions, briefs, and other materials filed or issued in administrative adjudication proceedings on their websites and in maintaining more comprehensive online collections of such adjudication materials. Part I provides an overview of federal administrative adjudication and the laws and policies relevant to the online disclosure of adjudication materials. Part II summarizes a survey the author conducted of 24 federal agency websites and presents its results. Part III analyzes the survey’s findings, dividing the analysis into two sections. The first section discusses the degree of accessibility of adjudication materials on agency websites by assessing the general ease of navigating to adjudication materials on the surveyed websites. The second section discusses the general disclosure practices of agency websites. Part III also relays key points derived from telephone and e-mail discussions with personnel from the Federal Maritime Commission, Consumer Product Safety Commission, and National Labor Relations Board. Next, Part IV presents case studies of the Federal Trade Commission, Federal Mine Safety & Health Review Commission, and Social Security Administration’s websites. These websites, each of which sits on a different point on the continuum of comprehensiveness and navigability that was revealed during the survey, are helpful in understanding the general range of agency practices. Lastly, Part V offers the author’s recommendations and best practices

The OU Linked Open Data: production and consumption

The aim of this paper is to introduce the current efforts toward the release and exploitation of The Open University's (OU) Linked Open Data (LOD). We introduce the work that has been done within the LUCERO project in order to select, extract and structure subsets of information contained within the OU data sources and migrate and expose this information as part of the LOD cloud. To show the potential of such exposure we also introduce three different prototypes that exploit this new educational resource: (1) the OU expert search system, a tool focused on fnding the best experts for a certain topic within the OU staff; (2) the Buddy Study system, a tool that relies on Facebook information to identify common interest among friends and recommend potential courses within the OU that `buddies' can study together, and; (3) Linked OpenLearn, an application that enables exploring linked courses, Podcasts and tags to OpenLearn units. Its aim is to enhance the browsing experience for students, by detecting relevant educational resources on fly while reading an OpenLearn unit