1,178 research outputs found
Organizing information on the next generation web - Design and implementation of a new bookmark structure
The next-generation Web will increase the need for a highly organized and ever evolving method to store references to Web objects. These requirements could be realized by the development of a new bookmark structure. This paper endeavors to identify the key requirements of such a bookmark, specifically in relation to Web documents, and sets out a suggested design through which these needs may be accomplished. A prototype developed offers such features as the sharing of bookmarks between users and groups of users. Bookmarks for Web documents in this prototype allow more specific information to be stored such as: URL, the document type, the document title, keywords, a summary, user annotations, date added, date last visited and date last modified. Individuals may access the service from anywhere on the Internet, as long as they have a Java-enabled Web browser
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
Agents, Bookmarks and Clicks: A topical model of Web traffic
Analysis of aggregate and individual Web traffic has shown that PageRank is a
poor model of how people navigate the Web. Using the empirical traffic patterns
generated by a thousand users, we characterize several properties of Web
traffic that cannot be reproduced by Markovian models. We examine both
aggregate statistics capturing collective behavior, such as page and link
traffic, and individual statistics, such as entropy and session size. No model
currently explains all of these empirical observations simultaneously. We show
that all of these traffic patterns can be explained by an agent-based model
that takes into account several realistic browsing behaviors. First, agents
maintain individual lists of bookmarks (a non-Markovian memory mechanism) that
are used as teleportation targets. Second, agents can retreat along visited
links, a branching mechanism that also allows us to reproduce behaviors such as
the use of a back button and tabbed browsing. Finally, agents are sustained by
visiting novel pages of topical interest, with adjacent pages being more
topically related to each other than distant ones. This modulates the
probability that an agent continues to browse or starts a new session, allowing
us to recreate heterogeneous session lengths. The resulting model is capable of
reproducing the collective and individual behaviors we observe in the empirical
data, reconciling the narrowly focused browsing patterns of individual users
with the extreme heterogeneity of aggregate traffic measurements. This result
allows us to identify a few salient features that are necessary and sufficient
to interpret the browsing patterns observed in our data. In addition to the
descriptive and explanatory power of such a model, our results may lead the way
to more sophisticated, realistic, and effective ranking and crawling
algorithms.Comment: 10 pages, 16 figures, 1 table - Long version of paper to appear in
Proceedings of the 21th ACM conference on Hypertext and Hypermedi
Exploiting Social Annotation for Automatic Resource Discovery
Information integration applications, such as mediators or mashups, that
require access to information resources currently rely on users manually
discovering and integrating them in the application. Manual resource discovery
is a slow process, requiring the user to sift through results obtained via
keyword-based search. Although search methods have advanced to include evidence
from document contents, its metadata and the contents and link structure of the
referring pages, they still do not adequately cover information sources --
often called ``the hidden Web''-- that dynamically generate documents in
response to a query. The recently popular social bookmarking sites, which allow
users to annotate and share metadata about various information sources, provide
rich evidence for resource discovery. In this paper, we describe a
probabilistic model of the user annotation process in a social bookmarking
system del.icio.us. We then use the model to automatically find resources
relevant to a particular information domain. Our experimental results on data
obtained from \emph{del.icio.us} show this approach as a promising method for
helping automate the resource discovery task.Comment: 6 pages, submitted to AAAI07 workshop on Information Integration on
the We
The Potential of Bookmark Based User Profiles
Driven by the explosive growth of information available online, the World-Wide-Web is currently witnessing a trend towards personalized information access. As part of this trend, numerous personalized news services are emerging. The goal of this project is to develop a prototype algorithm for using bookmarks to develop a personal profile. Ultimately, we imagine this might be used to construct a personalized RSS reader for reading news online. A reader returns a large number of news stories. To increase user satisfaction it is useful to rank them to bring the most interesting to the fore. This ranking is done by implementing a personalized profile. One way to create such a profile might be to extract it from user's bookmarks. In this paper, we describe a process for learning user interest from bookmarks and present an evaluation of its effectiveness. The goal is to utilize a user profile based on bookmarks to personalize results by filtering and re-ranking the entries returned from a set of user defined feeds
Exploring The Value Of Folksonomies For Creating Semantic Metadata
Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources
FolkRank: A Ranking Algorithm for Folksonomies
In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006
Privacy Preserving Internet Browsers: Forensic Analysis of Browzar
With the advance of technology, Criminal Justice agencies are being
confronted with an increased need to investigate crimes perpetuated partially
or entirely over the Internet. These types of crime are known as cybercrimes.
In order to conceal illegal online activity, criminals often use private
browsing features or browsers designed to provide total browsing privacy. The
use of private browsing is a common challenge faced in for example child
exploitation investigations, which usually originate on the Internet. Although
private browsing features are not designed specifically for criminal activity,
they have become a valuable tool for criminals looking to conceal their online
activity. As such, Technological Crime units often focus their forensic
analysis on thoroughly examining the web history on a computer. Private
browsing features and browsers often require a more in-depth, post mortem
analysis. This often requires the use of multiple tools, as well as different
forensic approaches to uncover incriminating evidence. This evidence may be
required in a court of law, where analysts are often challenged both on their
findings and on the tools and approaches used to recover evidence. However,
there are very few research on evaluating of private browsing in terms of
privacy preserving as well as forensic acquisition and analysis of privacy
preserving internet browsers. Therefore in this chapter, we firstly review the
private mode of popular internet browsers. Next, we describe the forensic
acquisition and analysis of Browzar, a privacy preserving internet browser and
compare it with other popular internet browser
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
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