2,563 research outputs found
URLs in the OPAC : comparative reflections on US vs UK practice
To examine whether placing URLs into library OPACs has been an effective way of enhancing the role of the catalogue for the contemporary library user
PageRank: Standing on the shoulders of giants
PageRank is a Web page ranking technique that has been a fundamental
ingredient in the development and success of the Google search engine. The
method is still one of the many signals that Google uses to determine which
pages are most important. The main idea behind PageRank is to determine the
importance of a Web page in terms of the importance assigned to the pages
hyperlinking to it. In fact, this thesis is not new, and has been previously
successfully exploited in different contexts. We review the PageRank method and
link it to some renowned previous techniques that we have found in the fields
of Web information retrieval, bibliometrics, sociometry, and econometrics
Relating Web pages to enable information-gathering tasks
We argue that relationships between Web pages are functions of the user's
intent. We identify a class of Web tasks - information-gathering - that can be
facilitated by a search engine that provides links to pages which are related
to the page the user is currently viewing. We define three kinds of intentional
relationships that correspond to whether the user is a) seeking sources of
information, b) reading pages which provide information, or c) surfing through
pages as part of an extended information-gathering process. We show that these
three relationships can be productively mined using a combination of textual
and link information and provide three scoring mechanisms that correspond to
them: {\em SeekRel}, {\em FactRel} and {\em SurfRel}. These scoring mechanisms
incorporate both textual and link information. We build a set of capacitated
subnetworks - each corresponding to a particular keyword - that mirror the
interconnection structure of the World Wide Web. The scores are computed by
computing flows on these subnetworks. The capacities of the links are derived
from the {\em hub} and {\em authority} values of the nodes they connect,
following the work of Kleinberg (1998) on assigning authority to pages in
hyperlinked environments. We evaluated our scoring mechanism by running
experiments on four data sets taken from the Web. We present user evaluations
of the relevance of the top results returned by our scoring mechanisms and
compare those to the top results returned by Google's Similar Pages feature,
and the {\em Companion} algorithm proposed by Dean and Henzinger (1999).Comment: In Proceedings of ACM Hypertext 200
An integrated ranking algorithm for efficient information computing in social networks
Social networks have ensured the expanding disproportion between the face of
WWW stored traditionally in search engine repositories and the actual ever
changing face of Web. Exponential growth of web users and the ease with which
they can upload contents on web highlights the need of content controls on
material published on the web. As definition of search is changing,
socially-enhanced interactive search methodologies are the need of the hour.
Ranking is pivotal for efficient web search as the search performance mainly
depends upon the ranking results. In this paper new integrated ranking model
based on fused rank of web object based on popularity factor earned over only
valid interlinks from multiple social forums is proposed. This model identifies
relationships between web objects in separate social networks based on the
object inheritance graph. Experimental study indicates the effectiveness of
proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC),
Vol.3, No.1, March 201
Applying digital content management to support localisation
The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM
Using the Co-Citation Network to Indicate Article Impact
Scholarly outputs are growing in number and frequency, driving the requirement to research new early indication metrics. Historically, citations have been used as an independent indication of the significance of scholarly material. However, citations are very slow to accrue since they can only be made by subsequently published material. This enforces a delay of a number of years before the citation impact of a publication can be accurately judged. Existing early indication metrics, such as download metrics and web based link analysis, have obtained correlation results. Brody finds a good correlation between download metrics and subsequent impact by citation, while Thelwall finds very little correlation between Google's PageRank and the number of links (or citations) to a web site, suggesting neither is a good surrogate indicator for the other. While valid studies, neither take account of the quality assessment factor of peer-review citation. This work presents an investigation into new metrics which utilize the co-citation network in order to rate a publications impact
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
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