مصورسازی تعاملی برای کاوش فرصت طلبانه مجموعه های بزرگ سند
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20116||2010||10 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Information Systems, Volume 35, Issue 2, April 2010, Pages 260–269
Finding relevant information in a large and comprehensive collection of cross-referenced documents like Wikipedia usually requires a quite accurate idea where to look for the pieces of data being sought. A user might not yet have enough domain-specific knowledge to form a precise search query to get the desired result on the first try. Another problem arises from the usually highly cross-referenced structure of such document collections. When researching a subject, users usually follow some references to get additional information not covered by a single document. With each document, more opportunities to navigate are added and the structure and relations of the visited documents gets harder to understand. This paper describes the interactive visualization Wivi which enables users to intuitively navigate Wikipedia by visualizing the structure of visited articles and emphasizing relevant other topics. Combining this visualization with a view of the current article results in a custom browser specially adapted for exploring large information networks. By visualizing the potential paths that could be taken, users are invited to read up on subjects relevant to the current point of focus and thus opportunistically finding relevant information. Results from a user study indicate that this visual navigation can be easily used and understood. A majority of the participants of the study stated that this method of exploration supports them finding information in Wikipedia.
A common approach to collect information about a specific subject in large, cross-referenced document collections like the Wikipedia is to start with an already known term and open the associated article. Within the article, links to other terms probably relevant to the research are found. If more than one article has to be read to get the desired information, a user has to follow some of the links to other articles. They usually have to be read one at a time and again contain links to further articles probably relevant to the subject. While navigating between several articles, a user has to keep track of what they already have read and how the piece of information they are currently reading relates to everything they already have read. Additionally, a user might want to know what other links they encountered in all the previously read articles and which therefore are probably worth following, especially if many of them lead to a single article. The problem arises from the complex structure of highly cross-referenced articles. They form a directed graph, which consists of hundreds of thousands of articles and usually significantly more links. The English language version of Wikipedia currently comprises 3 million articles and over 70 million links between them . Common web-browsers used for exploring web-based information resources are not providing any means to help users with the specific tasks presented when researching a subject in such a large network of articles. They only provide a history of visited pages which can be navigated forwards and backwards. Besides this simple linear history of pages, they do not tell the users what other links they might consider following and how two different pages are related to each other. In this paper, we present a navigation concept for the exploration of such large information resources, which visualizes the structure of articles a user has already seen and where a user might find further information related to the already researched subject. While there have been similar approaches to on-line, interactive visualizations of hypertext document structures in general, our work focuses on the additional visualization of all related links a user might want to follow, and how to help with choosing potentially interesting articles. By applying a weighting function to the yet unread articles, we can highlight more important articles and help a user to opportunistically explore the vast amount of information. This paper is organized as follows. In Section 2 we will review other approaches taken to interactively visualize large document structures. Section 3 will briefly explain the concept of opportunistic exploration and how we are applying it to the task of searching for information. The visualization and interaction concept we developed is then described in Section 4. Section 5 is outlining the implementation of our concept in the web-based application Wivi. 1 In Section 6, the set-up and results of the user study we conducted are described and analyzed respectively. In Section 7, we finally give our conclusion and present potential future work.
نتیجه گیری انگلیسی
As previous research has shown, interactive visualization of large document collections can be used to improve navigation and finding relevant information. Our approach combines both a visualization of visited articles and articles that could be immediately reached from all visited articles. It also calculates a degree of interest of the unvisited articles based on the structure and history of the article graph. With Wivi 2 we created a browser for the Wikipedia or other wikis, which implements the visualization of already visited articles in a hierarchical tree layout and shows the related unvisited articles weighted by their degree of interest on circles around the visited articles. As the result of a user test shows, this approach is generally accepted and positively perceived as a viable interface to browse and search the Wikipedia. Especially the visualization of the visited part was well received, but also our concept of weighting and displaying the unvisited articles to enable opportunistic exploration appears to be promising. As future work it would be interesting to explore other ways to visualize the unvisited articles and how the underlying weighting might be improved. One way to improve the weighting might be to take the categories of the articles into account, which provide some sort of clustering of articles. Also, it would be interesting to know how different approaches to extract the links between articles affect how well the concept of opportunistic exploration works. While Wivi uses a simple way to retrieve other articles, the implementation of more sophisticated methods could be easily integrated.