Introduction TheInternetandWWWhavealreadyestablishedthemselvesasmajorfactorsintheoperationofscholarlycom - munitiesworldwide.Today,theInternetisusedinall spheresoflifeforexchangeofinformation.Information resourcesontheInternetareincreasingtremendously.GordonandPathak (1999)suggestedthattheprimaryuseofthe Internetisforinformationretrieval.Searchenginesare consideredasthemostimportanttoolforretrievinginformationontheWeb, andconsequentlyformacriticalareaof research(Gaines,Chen,&Shaw,1997;Lawrence&Giles, 1998). DespitetheeffectivenessofInternet-basedoronlineinformationretrieval, problemsstillexist.Woodward(1996) arguedthattheInternetiscurrentlyinastateofnearchaos intermsofaccessandorganizationofinformation.Voorbij (1999)foundthat67%oftheInternetusersagreeor stronglyagreewiththedifficultytoperformsubject searchesontheInternet.Users,especiallythenoviceand irregularusers,finditdifficulttophrasetheirinformation needsduetothelackofknowledgeliteracyinsearch domain(Bates,1986,1998).Alth

Design is a term that brings many people together. Collectively, we distinguish ourselves from others by the fact that we are designers and members of a design community. But, design is also a term that pushes people apart. The design that some value in the new fashions in the boutiques in Milan is not seen by everyone as design. While some are impressed with the design of a new telephone, not everyone sees this as design. As a community, we believe design is important. But, as a community, we do not have a common definition of what it is. Many views of design have been proposed. Several classifications of design have been proposed. In this paper, we also seek to classify views on design. Unlike earlier efforts, however, we want to find the classification that the global community of designers uses. To this end, we examine the patterns of citations to key authors ’ works (Author Cocitation Analysis) to uncover this classification and identify seven key author clusters representing identifiable theory groups or schools of thought/practice in design.

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”jaguars ” returns results as diverse as web sites about cars, computer games, attack planes, American football, and animals. More and more search engines offer options to organize query results by categories or, given a document, to return a list of links to topically related documents. While information retrieval traditionally defines similarity of documents in terms of contents, it seems natural to expect that the very structure of the Web carries important information about the topical similarity of documents. Here we study the role of a matrix constructed from weighted cocitations (documents referenced by the same document), weighted couplings (documents referencing the same document), incoming, and outgoing links for the clustering of documents on the Web. We present and discuss three methods of clustering based on this matrix construction using three clustering algorithms, K-means, Markov and Maximum Spanning Tree, respectively. Our main contribution is a clustering technique based on the Maximum Spanning Tree technique and an evaluation of its effectiveness comparatively to the two most robust alternatives: K-means and Markov clustering. Index Terms — Search engines, clustering, co-citation, coupling, hyperlinks

The move towards integrated international Digital Libraries o#ers the opportunity of creating comprehensive data on citation networks.

We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees (MSTs) and a two-dimensional map of scientific journals using the database of the Science Citation Index (SCI) during 1994–2001. The structures of constructed MSTs are consistent with the sorting of SCI categories. The map of science is constructed based on our MST results. Such a map shows the relation among various knowledge clusters and their citation properties. The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences, life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to various levels of knowledge domains.

Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and

Science-Metrix specializes in the measurement and evaluation of science, technology and innovation. Our data collection and assessment methods include bibliometrics, scientometrics, technometrics, surveys and interviews, environmnetal scans, monitoring and intelligence gathering. We perform program and policy evaluations, benchmarking and sector analyses, market studies and strategic planning. Science-Metrix has a robust knowledge of life and environmental sciences.

We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-linked document databases. Our method can scale to large datasets because it exploits the underlying regularity often found in hyper-linked document databases. Because of this scalability, we can use our method to study the temporal trends of individual clusters in a statistically meaningful manner. As an example of our approach, we give a summary of the temporal trends found in a scientific literature database with thousands of documents. 1 Introduction Over the past decade, the World Wide Web has become an increasingly popular medium for publishing scientific literature. Since many researchers release preprints on the web, the scientific literature on the web is often far more timely than a similar snapshot of paper journals and proceedings, especially when one considers review and publication delays. As such, the scientific literature on the web may represent one of the more up-to-da...

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