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  1. Rasmussen, E.M.: Indexing and retrieval for the Web (2002) 0.04
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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).
    Source
    Annual review of information science and technology. 37(2003), S.91-126
  2. Yang, K.: Information retrieval on the Web (2004) 0.04
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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.
    Source
    Annual review of information science and technology. 39(2005), S.33-80
  3. Bar-Ilan, J.: ¬The use of Web search engines in information science research (2003) 0.03
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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.
    Source
    Annual review of information science and technology. 38(2004), S.231-288
  4. Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003) 0.03
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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.
    Source
    Annual review of information science and technology. 38(2004), S.289-330
  5. Chowdhury, G.G.: Natural language processing (2002) 0.03
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    Abstract
    Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. NLP researchers aim to gather knowledge an how human beings understand and use language so that appropriate tools and techniques can be developed to make computer systems understand and manipulate natural languages to perform desired tasks. The foundations of NLP lie in a number of disciplines, namely, computer and information sciences, linguistics, mathematics, electrical and electronic engineering, artificial intelligence and robotics, and psychology. Applications of NLP include a number of fields of study, such as machine translation, natural language text processing and summarization, user interfaces, multilingual and cross-language information retrieval (CLIR), speech recognition, artificial intelligence, and expert systems. One important application area that is relatively new and has not been covered in previous ARIST chapters an NLP relates to the proliferation of the World Wide Web and digital libraries.
    Source
    Annual review of information science and technology. 37(2003), S.51-90
  6. Legg, C.: Ontologies on the Semantic Web (2007) 0.02
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    Abstract
    As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The "Semantic Web" is touted by its developers as equally revolutionary, although it has not yet achieved anything like the Web's exponential uptake. It seeks to transcend a current limitation of the Web - that it largely requires indexing to be accomplished merely on specific character strings. Thus, a person searching for information about "turkey" (the bird) receives from current search engines many irrelevant pages about "Turkey" (the country) and nothing about the Spanish "pavo" even if he or she is a Spanish-speaker able to understand such pages. The Semantic Web vision is to develop technology to facilitate retrieval of information via meanings, not just spellings. For this to be possible, most commentators believe, Semantic Web applications will have to draw on some kind of shared, structured, machine-readable conceptual scheme. Thus, there has been a convergence between the Semantic Web research community and an older tradition with roots in classical Artificial Intelligence (AI) research (sometimes referred to as "knowledge representation") whose goal is to develop a formal ontology. A formal ontology is a machine-readable theory of the most fundamental concepts or "categories" required in order to understand information pertaining to any knowledge domain. A review of the attempts that have been made to realize this goal provides an opportunity to reflect in interestingly concrete ways on various research questions such as the following: - How explicit a machine-understandable theory of meaning is it possible or practical to construct? - How universal a machine-understandable theory of meaning is it possible or practical to construct? - How much (and what kind of) inference support is required to realize a machine-understandable theory of meaning? - What is it for a theory of meaning to be machine-understandable anyway?
    Source
    Annual review of information science and technology. 41(2007), S.407-451
    Theme
    Semantic Web
  7. Chang, S.J.; Rice, R.R.: Browsing: a multidimensional framework (1993) 0.02
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    Abstract
    State of the art review of browsing from many different multidisciplinary contexts, integrating the diverse literatures on browsing: library and information science (information searching); end user information retrieval and system design (database searching); consumer behaviour (store shopping); mass media audience (television channel switching); organizational communication; and wayfinding and environmental design. Considers what constitutes browsing, and what are the consequences of browsing. Attempts to identify the underlying common dimensions of browsing and the consequences of browsing in a wide variety of human activities
    Imprint
    Medford, NJ : Learned Information
    Source
    Annual review of information science and technology. 28(1993), S.231-276
  8. Smith, L.C.: Artificial intelligence and information retrieval (1987) 0.02
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    Source
    Annual review of information science and technology. 22(1987), S.41-77
  9. Marsh, S.; Dibben, M.R.: ¬The role of trust in information science and technology (2002) 0.02
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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.
    Source
    Annual review of information science and technology. 37(2003), S.465-498
  10. Dumais, S.T.: Latent semantic analysis (2003) 0.02
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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.
    Source
    Annual review of information science and technology. 38(2004), S.189-230
  11. Enser, P.G.B.: Visual image retrieval (2008) 0.02
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    Date
    22. 1.2012 13:01:26
    Source
    Annual review of information science and technology. 42(2008), S.3-42
  12. Belkin, N.J.; Croft, W.B.: Retrieval techniques (1987) 0.02
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    Source
    Annual review of information science and technology. 22(1987), S.109-145
  13. Kling, R.; Callahan, E.: Electronic journals, the Internet, and scholarly communication (2002) 0.02
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    Form
    Elektronische Dokumente
    Source
    Annual review of information science and technology. 37(2003), S.127-178
  14. Julien, C.-A.; Leide, J.E.; Bouthillier, F.: Controlled user evaluations of information visualization interfaces for text retrieval : literature review and meta-analysis (2008) 0.01
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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.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.6, S.1012-1024
  15. Hjoerland, B.; Kyllesbech Nielsen, L.: Subject access points in electronic retrieval (2001) 0.01
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    Source
    Annual review of information science and technology. 35(2001), S.249-298
    Theme
    Klassifikationssysteme im Online-Retrieval
    Verbale Doksprachen im Online-Retrieval
  16. Buckland, M.K.; Liu, Z.: History of information science (1995) 0.01
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    Abstract
    State of the art review of the historical development of information science as deemed to be covered by the particular interests of memebers of the American Society for Information Science, as defined as the representation, storage, transmission, selection, retrieval, filtering, and use of documents and messages. Arranges the references cited roughly according to the classification scheme used by Information Science Abstracts, and so uses the headings: background; information science; techniques and technology; information related behaviour; application areas; social aspects; education for information science; institutions; individuals; geographical areas; and conclusions
    Date
    13. 6.1996 19:22:20
    Source
    Annual review of information science and technology. 30(1995), S.385-416
  17. Large, A.: Children, teenagers, and the Web (2004) 0.01
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    Annual review of information science and technology. 39(2005), S.347-394
  18. Bookstein, A.: Probability and Fuzzy-set applications to information retrieval (1985) 0.01
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    Annual review of information science and technology. 20(1985), S.117-151
  19. Ruthven, R.: Interactive information retrieval (2008) 0.01
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    Annual review of information science and technology. 42(2008), S.xxx-xxx
  20. Foster, J.: Collaborative information seeking and retrieval (2006) 0.01
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    Annual review of information science and technology. 40(2006), S.xxx-xxx

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