Search (95 results, page 4 of 5)

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Abstract

The World Wide Web was created in 1989, but it has already become a major information channel and source, influencing our everyday lives, commercial transactions, and scientific communication, to mention just a few areas. The seventeenth-century philosopher Descartes proclaimed, "I think, therefore I am" (cogito, ergo sum). Today the Web is such an integral part of our lives that we could rephrase Descartes' statement as "I have a Web presence, therefore I am." Because many people, companies, and organizations take this notion seriously, in addition to more substantial reasons for publishing information an the Web, the number of Web pages is in the billions and growing constantly. However, it is not sufficient to have a Web presence; tools that enable users to locate Web pages are needed as well. The major tools for discovering and locating information an the Web are search engines. This review discusses the use of Web search engines in information science research. Before going into detail, we should define the terms "information science," "Web search engine," and "use" in the context of this review.

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Abstract

This chapter discusses the notion of trust as it relates to information science and technology, specifically user interfaces, autonomous agents, and information systems. We first present an in-depth discussion of the concept of trust in and of itself, moving an to applications and considerations of trust in relation to information technologies. We consider trust from a "soft" perspective-thus, although security concepts such as cryptography, virus protection, authentication, and so forth reinforce (or damage) the feelings of trust we may have in a system, they are not themselves constitutive of "trust." We discuss information technology from a human-centric viewpoint, where trust is a less well-structured but much more powerful phenomenon. With the proliferation of electronic commerce (e-commerce) and the World Wide Web (WWW, or Web), much has been made of the ability of individuals to explore the vast quantities of information available to them, to purchase goods (as diverse as vacations and cars) online, and to publish information an their personal Web sites.

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Abstract

The literature of the evaluation of Internet search engines is reviewed. Although there have been many studies, there has been little consistency in the way such studies have been carried out. This problem is exacerbated by the fact that recall is virtually impossible to calculate in the fast changing Internet environment, and therefore the traditional Cranfield type of evaluation is not usually possible. A variety of alternative evaluation methods has been suggested to overcome this difficulty. The authors recommend that a standardised set of tools is developed for the evaluation of web search engines so that, in future, comparisons can be made between search engines more effectively, and that variations in performance of any given search engine over time can be tracked. The paper itself does not provide such a standard set of tools, but it investigates the issues and makes preliminary recommendations of the types of tools needed

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Abstract

This review describes experimental designs (users, search tasks, measures, etc.) used by 31 controlled user studies of information visualization (IV) tools for textual information retrieval (IR) and a meta-analysis of the reported statistical effects. Comparable experimental designs allow research designers to compare their results with other reports, and support the development of experimentally verified design guidelines concerning which IV techniques are better suited to which types of IR tasks. The studies generally use a within-subject design with 15 or more undergraduate students performing browsing to known-item tasks on sets of at least 1,000 full-text articles or Web pages on topics of general interest/news. Results of the meta-analysis (N = 8) showed no significant effects of the IV tool as compared with a text-only equivalent, but the set shows great variability suggesting an inadequate basis of comparison. Experimental design recommendations are provided which would support comparison of existing IV tools for IR usability testing.

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Abstract

This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2004 with a keyword index and article abstract in order to explore how KM performance evaluation has developed during this period. Based on the scope of 76 articles from 78 academic journals of KM, this paper surveys and classifies KM measurements using the following eight categories: qualitative analysis, quantitative analysis, financial indicator analysis, non-financial indicator analysis, internal performance analysis, external performance analysis, project-oriented analysis, and organizational-oriented analysis together with their measurement matrices for different research and problem domains. Discussion is presented, indicating the followings future development directions for KM performance evaluation: (1) KM performance evaluation is getting more important. (2) The quantitative analysis is the primary methodology in KM performance evaluation. (3) Firms are now highlighting the KM performance of competitors, through benchmarking or best practices, rather than internally auditing KM performance via balanced scorecard. (4) Firms may begin to focus more on project management measurement, than on the entire organization.

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Abstract

The aim of this chapter is to demonstrate that semantic issues underlie all research questions within Library and Information Science (LIS, or, as hereafter, IS) and, in particular, the subfield known as Knowledge Organization (KO). Further, it seeks to show that semantics is a field influenced by conflicting views and discusses why it is important to argue for the most fruitful one of these. Moreover, the chapter demonstrates that IS has not yet addressed semantic problems in systematic fashion and examines why the field is very fragmented and without a proper theoretical basis. The focus here is on broad interdisciplinary issues and the long-term perspective. The theoretical problems involving semantics and concepts are very complicated. Therefore, this chapter starts by considering tools developed in KO for information retrieval (IR) as basically semantic tools. In this way, it establishes a specific IS focus on the relation between KO and semantics. It is well known that thesauri consist of a selection of concepts supplemented with information about their semantic relations (such as generic relations or "associative relations"). Some words in thesauri are "preferred terms" (descriptors), whereas others are "lead-in terms." The descriptors represent concepts. The difference between "a word" and "a concept" is that different words may have the same meaning and similar words may have different meanings, whereas one concept expresses one meaning.

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Abstract

Latent Semantic Analysis (LSA) was first introduced in Dumais, Furnas, Landauer, and Deerwester (1988) and Deerwester, Dumais, Furnas, Landauer, and Harshman (1990) as a technique for improving information retrieval. The key insight in LSA was to reduce the dimensionality of the information retrieval problem. Most approaches to retrieving information depend an a lexical match between words in the user's query and those in documents. Indeed, this lexical matching is the way that the popular Web and enterprise search engines work. Such systems are, however, far from ideal. We are all aware of the tremendous amount of irrelevant information that is retrieved when searching. We also fail to find much of the existing relevant material. LSA was designed to address these retrieval problems, using dimension reduction techniques. Fundamental characteristics of human word usage underlie these retrieval failures. People use a wide variety of words to describe the same object or concept (synonymy). Furnas, Landauer, Gomez, and Dumais (1987) showed that people generate the same keyword to describe well-known objects only 20 percent of the time. Poor agreement was also observed in studies of inter-indexer consistency (e.g., Chan, 1989; Tarr & Borko, 1974) in the generation of search terms (e.g., Fidel, 1985; Bates, 1986), and in the generation of hypertext links (Furner, Ellis, & Willett, 1999). Because searchers and authors often use different words, relevant materials are missed. Someone looking for documents an "human-computer interaction" will not find articles that use only the phrase "man-machine studies" or "human factors." People also use the same word to refer to different things (polysemy). Words like "saturn," "jaguar," or "chip" have several different meanings. A short query like "saturn" will thus return many irrelevant documents. The query "Saturn Gar" will return fewer irrelevant items, but it will miss some documents that use only the terms "Saturn automobile." In searching, there is a constant tension between being overly specific and missing relevant information, and being more general and returning irrelevant information.
A number of approaches have been developed in information retrieval to address the problems caused by the variability in word usage. Stemming is a popular technique used to normalize some kinds of surface-level variability by converting words to their morphological root. For example, the words "retrieve," "retrieval," "retrieved," and "retrieving" would all be converted to their root form, "retrieve." The root form is used for both document and query processing. Stemming sometimes helps retrieval, although not much (Harman, 1991; Hull, 1996). And, it does not address Gases where related words are not morphologically related (e.g., physician and doctor). Controlled vocabularies have also been used to limit variability by requiring that query and index terms belong to a pre-defined set of terms. Documents are indexed by a specified or authorized list of subject headings or index terms, called the controlled vocabulary. Library of Congress Subject Headings, Medical Subject Headings, Association for Computing Machinery (ACM) keywords, and Yellow Pages headings are examples of controlled vocabularies. If searchers can find the right controlled vocabulary terms, they do not have to think of all the morphologically related or synonymous terms that authors might have used. However, assigning controlled vocabulary terms in a consistent and thorough manner is a time-consuming and usually manual process. A good deal of research has been published about the effectiveness of controlled vocabulary indexing compared to full text indexing (e.g., Bates, 1998; Lancaster, 1986; Svenonius, 1986). The combination of both full text and controlled vocabularies is often better than either alone, although the size of the advantage is variable (Lancaster, 1986; Markey, Atherton, & Newton, 1982; Srinivasan, 1996). Richer thesauri have also been used to provide synonyms, generalizations, and specializations of users' search terms (see Srinivasan, 1992, for a review). Controlled vocabularies and thesaurus entries can be generated either manually or by the automatic analysis of large collections of texts.
With the advent of large-scale collections of full text, statistical approaches are being used more and more to analyze the relationships among terms and documents. LSA takes this approach. LSA induces knowledge about the meanings of documents and words by analyzing large collections of texts. The approach simultaneously models the relationships among documents based an their constituent words, and the relationships between words based an their occurrence in documents. By using fewer dimensions for representation than there are unique words, LSA induces similarities among terms that are useful in solving the information retrieval problems described earlier. LSA is a fully automatic statistical approach to extracting relations among words by means of their contexts of use in documents, passages, or sentences. It makes no use of natural language processing techniques for analyzing morphological, syntactic, or semantic relations. Nor does it use humanly constructed resources like dictionaries, thesauri, lexical reference systems (e.g., WordNet), semantic networks, or other knowledge representations. Its only input is large amounts of texts. LSA is an unsupervised learning technique. It starts with a large collection of texts, builds a term-document matrix, and tries to uncover some similarity structures that are useful for information retrieval and related text-analysis problems. Several recent ARIST chapters have focused an text mining and discovery (Benoit, 2002; Solomon, 2002; Trybula, 2000). These chapters provide complementary coverage of the field of text analysis.

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Abstract

This classified critical bibliography continues the work initiated by Monika Kirtland and Pauline Cochrane, and furthered by Steven Blake Shubert. Kirtland and Cochrane published a bibliography surveying the literature critical of LCSH from 1944-1979 titled "Critical Views of LCSH - Library of Congress Subject Headings, A Bibliographic and Bibliometric Essay." Shubert analyzed another decade of literature in his article titled "Critical Views of LCSH-Ten Years Later: A Bibliographic Essay." This current bibliography compiles the next twelve years of critical literature from 1990-2001. Persistent concerns of the past fifty-seven years include inadequate syndetic structure, currency or bias of the headings, and lack of specificity in the subject heading list. New developments and research are in the areas of subdivisions, mapping, indexer inconsistency, and post-coordination. LCSH must become more flexible and easier to use in order to increase its scalability and interoperability as an online subject searching tool.

Footnote

Vgl. auch die Vorgänger: Kirtland, M., P.A. Cochrane: Critical views of LCSH - Library of Congress Subject Headings: a bibliographic and bibliometric essay. In: Cataloging and classification quarterly. 1(1982) no.2/3, S.71-93. Shubert, S.B.: Critical views of LCSH - ten years later: a bibliographic essay. In: Cataloging and classification quarterly. 15(1992) no.2, S.37-97.

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Abstract

The introduction and growth of the World Wide Web (WWW, or Web) have resulted in a profound change in the way individuals and organizations access information. In terms of volume, nature, and accessibility, the characteristics of electronic information are significantly different from those of even five or six years ago. Control of, and access to, this flood of information rely heavily an automated techniques for indexing and retrieval. According to Gudivada, Raghavan, Grosky, and Kasanagottu (1997, p. 58), "The ability to search and retrieve information from the Web efficiently and effectively is an enabling technology for realizing its full potential." Almost 93 percent of those surveyed consider the Web an "indispensable" Internet technology, second only to e-mail (Graphie, Visualization & Usability Center, 1998). Although there are other ways of locating information an the Web (browsing or following directory structures), 85 percent of users identify Web pages by means of a search engine (Graphie, Visualization & Usability Center, 1998). A more recent study conducted by the Stanford Institute for the Quantitative Study of Society confirms the finding that searching for information is second only to e-mail as an Internet activity (Nie & Ebring, 2000, online). In fact, Nie and Ebring conclude, "... the Internet today is a giant public library with a decidedly commercial tilt. The most widespread use of the Internet today is as an information search utility for products, travel, hobbies, and general information. Virtually all users interviewed responded that they engaged in one or more of these information gathering activities."
Techniques for automated indexing and information retrieval (IR) have been developed, tested, and refined over the past 40 years, and are well documented (see, for example, Agosti & Smeaton, 1996; BaezaYates & Ribeiro-Neto, 1999a; Frakes & Baeza-Yates, 1992; Korfhage, 1997; Salton, 1989; Witten, Moffat, & Bell, 1999). With the introduction of the Web, and the capability to index and retrieve via search engines, these techniques have been extended to a new environment. They have been adopted, altered, and in some Gases extended to include new methods. "In short, search engines are indispensable for searching the Web, they employ a variety of relatively advanced IR techniques, and there are some peculiar aspects of search engines that make searching the Web different than more conventional information retrieval" (Gordon & Pathak, 1999, p. 145). The environment for information retrieval an the World Wide Web differs from that of "conventional" information retrieval in a number of fundamental ways. The collection is very large and changes continuously, with pages being added, deleted, and altered. Wide variability between the size, structure, focus, quality, and usefulness of documents makes Web documents much more heterogeneous than a typical electronic document collection. The wide variety of document types includes images, video, audio, and scripts, as well as many different document languages. Duplication of documents and sites is common. Documents are interconnected through networks of hyperlinks. Because of the size and dynamic nature of the Web, preprocessing all documents requires considerable resources and is often not feasible, certainly not an the frequent basis required to ensure currency. Query length is usually much shorter than in other environments-only a few words-and user behavior differs from that in other environments. These differences make the Web a novel environment for information retrieval (Baeza-Yates & Ribeiro-Neto, 1999b; Bharat & Henzinger, 1998; Huang, 2000).

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Abstract

This chapter reviews visualization techniques that can be used to map the ever-growing domain structure of scientific disciplines and to support information retrieval and classification. In contrast to the comprehensive surveys conducted in traditional fashion by Howard White and Katherine McCain (1997, 1998), this survey not only reviews emerging techniques in interactive data analysis and information visualization, but also depicts the bibliographical structure of the field itself. The chapter starts by reviewing the history of knowledge domain visualization. We then present a general process flow for the visualization of knowledge domains and explain commonly used techniques. In order to visualize the domain reviewed by this chapter, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using tutorial style, we then apply various algorithms to demonstrate the visualization effectsl produced by different approaches and compare the results. The domain visualizations reveal the relationships within and between the four fields that together constitute the focus of this chapter. We conclude with a general discussion of research possibilities. Painting a "big picture" of scientific knowledge has long been desirable for a variety of reasons. Traditional approaches are brute forcescholars must sort through mountains of literature to perceive the outlines of their field. Obviously, this is time-consuming, difficult to replicate, and entails subjective judgments. The task is enormously complex. Sifting through recently published documents to find those that will later be recognized as important is labor intensive. Traditional approaches struggle to keep up with the pace of information growth. In multidisciplinary fields of study it is especially difficult to maintain an overview of literature dynamics. Painting the big picture of an everevolving scientific discipline is akin to the situation described in the widely known Indian legend about the blind men and the elephant. As the story goes, six blind men were trying to find out what an elephant looked like. They touched different parts of the elephant and quickly jumped to their conclusions. The one touching the body said it must be like a wall; the one touching the tail said it was like a snake; the one touching the legs said it was like a tree trunk, and so forth. But science does not stand still; the steady stream of new scientific literature creates a continuously changing structure. The resulting disappearance, fusion, and emergence of research areas add another twist to the tale-it is as if the elephant is running and dynamically changing its shape. Domain visualization, an emerging field of study, is in a similar situation. Relevant literature is spread across disciplines that have traditionally had few connections. Researchers examining the domain from a particular discipline cannot possibly have an adequate understanding of the whole. As noted by White and McCain (1997), the new generation of information scientists is technically driven in its efforts to visualize scientific disciplines. However, limited progress has been made in terms of connecting pioneers' theories and practices with the potentialities of today's enabling technologies. If the difference between past and present generations lies in the power of available technologies, what they have in common is the ultimate goal-to reveal the development of scientific knowledge.

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Abstract

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

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Abstract

Why devote an ARIST chapter to scholarly communication and bibliometrics, and why now? Bibliometrics already is a frequently covered ARIST topic, with chapters such as that by White and McCain (1989) on bibliometrics generally, White and McCain (1997) on visualization of literatures, Wilson and Hood (2001) on informetric laws, and Tabah (2001) on literature dynamics. Similarly, scholarly communication has been addressed in other ARIST chapters such as Bishop and Star (1996) on social informatics and digital libraries, Schamber (1994) on relevance and information behavior, and many earlier chapters on information needs and uses. More than a decade ago, the first author addressed the intersection of scholarly communication and bibliometrics with a journal special issue and an edited book (Borgman, 1990; Borgman & Paisley, 1989), and she recently examined interim developments (Borgman, 2000a, 2000c). This review covers the decade (1990-2000) since the comprehensive 1990 volume, citing earlier works only when necessary to explain the foundation for recent developments.

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Abstract

Data mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions. However, this definition may be applied equally to "knowledge discovery in databases" (KDD). Indeed, in the recent literature of DM and KDD, a source of confusion has emerged, making it difficult to determine the exact parameters of both. KDD is sometimes viewed as the broader discipline, of which data mining is merely a component-specifically pattern extraction, evaluation, and cleansing methods (Raghavan, Deogun, & Sever, 1998, p. 397). Thurasingham (1999, p. 2) remarked that "knowledge discovery," "pattern discovery," "data dredging," "information extraction," and "knowledge mining" are all employed as synonyms for DM. Trybula, in his ARIST chapter an text mining, observed that the "existing work [in KDD] is confusing because the terminology is inconsistent and poorly defined.

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Abstract

Relevance is a, if not even the, key notion in information science in general and information retrieval in particular. This two-part critical review traces and synthesizes the scholarship on relevance over the past 30 years and provides an updated framework within which the still widely dissonant ideas and works about relevance might be interpreted and related. It is a continuation and update of a similar review that appeared in 1975 under the same title, considered here as being Part I. The present review is organized into two parts: Part II addresses the questions related to nature and manifestations of relevance, and Part III addresses questions related to relevance behavior and effects. In Part II, the nature of relevance is discussed in terms of meaning ascribed to relevance, theories used or proposed, and models that have been developed. The manifestations of relevance are classified as to several kinds of relevance that form an interdependent system of relevances. In Part III, relevance behavior and effects are synthesized using experimental and observational works that incorporate data. In both parts, each section concludes with a summary that in effect provides an interpretation and synthesis of contemporary thinking on the topic treated or suggests hypotheses for future research. Analyses of some of the major trends that shape relevance work are offered in conclusions.

Content

Relevant: Having significant and demonstrable bearing on the matter at hand.[Note *][A version of this article has been published in 2006 as a chapter in E.G. Abels & D.A. Nitecki (Eds.), Advances in Librarianship (Vol. 30, pp. 3-71). San Diego: Academic Press. (Saracevic, 2006).] Relevance: The ability as of an information retrieval system to retrieve material that satisfies the needs of the user. - Merriam-Webster Dictionary 2005

Type

a

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Abstract

This bibliography contains selected cataloging tools an the Internet. It is divided into seven sections as follows: authority management and subject headings tools; cataloging tools by type of materials; dictionaries, encyclopedias, and place names; listservs and workshops; software and vendors; technical service professional organizations; and journals and newsletters. Resources are arranged in alphabetical order under each topic. Selected cataloging tools are annotated. There is some overlap since a given web site can cover many tools.

Type

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Abstract

How do we find information an the Web? Although information on the Web is distributed and decentralized, the Web can be viewed as a single, virtual document collection. In that regard, the fundamental questions and approaches of traditional information retrieval (IR) research (e.g., term weighting, query expansion) are likely to be relevant in Web document retrieval. Findings from traditional IR research, however, may not always be applicable in a Web setting. The Web document collection - massive in size and diverse in content, format, purpose, and quality - challenges the validity of previous research findings that are based an relatively small and homogeneous test collections. Moreover, some traditional IR approaches, although applicable in theory, may be impossible or impractical to implement in a Web setting. For instance, the size, distribution, and dynamic nature of Web information make it extremely difficult to construct a complete and up-to-date data representation of the kind required for a model IR system. To further complicate matters, information seeking on the Web is diverse in character and unpredictable in nature. Web searchers come from all walks of life and are motivated by many kinds of information needs. The wide range of experience, knowledge, motivation, and purpose means that searchers can express diverse types of information needs in a wide variety of ways with differing criteria for satisfying those needs. Conventional evaluation measures, such as precision and recall, may no longer be appropriate for Web IR, where a representative test collection is all but impossible to construct. Finding information on the Web creates many new challenges for, and exacerbates some old problems in, IR research. At the same time, the Web is rich in new types of information not present in most IR test collections. Hyperlinks, usage statistics, document markup tags, and collections of topic hierarchies such as Yahoo! (http://www.yahoo.com) present an opportunity to leverage Web-specific document characteristics in novel ways that go beyond the term-based retrieval framework of traditional IR. Consequently, researchers in Web IR have reexamined the findings from traditional IR research.

Type

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Abstract

What is an electronic record, how should it best be preserved and made available, and to what extent do traditional, paradigmatic archival precepts such as provenance, original order, and archival custody hold when managing it? Over more than four decades of work in the area of electronic records (formerly known as machine-readable records), theorists and researchers have offered answers to these questions-or at least devised approaches for trying to answer them. However, a set of fundamental questions about the nature of the record and the applicability of traditional archival theory still confronts researchers seeking to advance knowledge and development in this increasingly active, but contested, area of research. For example, which characteristics differentiate a record from other types of information objects (such as publications or raw research data)? Are these characteristics consistently present regardless of the medium of the record? Does the record always have to have a tangible form? How does the record manifest itself within different technological and procedural contexts, and in particular, how do we determine the parameters of electronic records created in relational, distributed, or dynamic environments that bear little resemblance an the surface to traditional paper-based environments? At the heart of electronic records research lies a dual concern with the nature of the record as a specific type of information object and the nature of legal and historical evidence in a digital world. Electronic records research is relevant to the agendas of many communities in addition to that of archivists. Its emphasis an accountability and an establishing trust in records, for example, addresses concerns that are central to both digital government and e-commerce. Research relating to electronic records is still relatively homogeneous in terms of scope, in that most major research initiatives have addressed various combinations of the following: theory building in terms of identifying the nature of the electronic record, developing alternative conceptual models, establishing the determinants of reliability and authenticity in active and preserved electronic records, identifying functional and metadata requirements for record keeping, developing and testing preservation

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Footnote

Rez. in: JASIST 54(2003) no.7, S.695-697 (R. Savolainen): "Donald O. Case has written an ambitious book to create an overall picture of the major approaches to information needs and seeking (INS) studies. The aim to write an extensive review is reflected in the list of references containing about 700 items. The high ambitions are explained an p. 14, where Case states that he is aiming at a multidisciplinary understanding of the concept of information seeking. In the Preface, the author characterizes his book as an introduction to the topic for students at the graduate level, as well as as a review and handbook for scholars engagged in information behavior research. In my view, Looking for Information is particularly welcome as an academic textbook because the field of INS studies suffers from the lack of monographs. Along with the continuous growth of the number of journal articles and conference papers, there is a genuine need for a book that picks up the numerous pieces and puts them together. The use of the study as a textbook is facilitated by clearly delineated sections an major themes and the wealth of concrete examples of information seeking in everyday contexts. The book is lucidly written and it is accessible to novice readers, too. At first glance, the idea of providing a comprehensive review of INS studies may seem a mission impossible because the current number of articles, papers, and other contributions in this field is nearing the 10,000 range (p. 224). Donald Case is not alone in the task of coming to grips with an increasing number of studies; similar problems have been faced by those writing INS-related chapters for the Annual Review of Information Science and Technology (ARIST). Case has solved the problem of "too many publications to be reviewed" by concentrating an the INS literature published during the last two decades. Secondly, studies an library use and information retrieval are discussed only to a limited extent. In addition, Case is highly selective as to studies focusing an the use of specific sources and channels such as WWW. These delineations are reasonable, even though they beg some questions. First, how should one draw the line between studies an information seeking and information retrieval? Case does not discuss this question in greater detail, although in recent years, the overlapping areas of information seeking and retrieval studies have been broadened, along with the growing importance of WWW in information seeking/retrieval. Secondly, how can one define the concept of information searching (or, more specifically, Internet or Web searching) in relation to information seeking and information retrieval? In the field of Web searching studies, there is an increasing number of contributions that are of direct relevance to information-seeking studies. Clearly, the advent of the Internet, particularly, the Web, has blurred the previous lines between INS and IR literature, making them less clear cut. The book consists of five main sections, and comprises 13 chapters. There is an Appendix serving the needs of an INS textbook (questions for discussion and application). The structure of the book is meticulously planned and, as a whole, it offers a sufficiently balanced contribution to theoretical, methodological, and empirical issues of INS. The title, Looking for Information: A Survey of Research an Information Seeking, Needs, and Behavior aptly describes the main substance of the book. . . . It is easy to agree with Case about the significance of the problem of specialization and fragmentation. This problem seems to be concomitant with the broadening field of INS research. In itself, Case's book can be interpreted as a struggle against this fragmentation. His book suggests that this struggle is not hopeless and that it is still possible to draw an overall picture of the evolving research field. The major pieces of the puzzle were found and the book will provide a useful overview of INS studies for many years."

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Abstract

Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.

Type

a