Search (91 results, page 1 of 5)

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Date

22. 1.2012 13:01:26

Source

Annual review of information science and technology. 42(2008), S.3-42

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Abstract

Imagine a world where you walk up to a computer and sing the song fragment that has been plaguing you since breakfast. The computer accepts your off-key singing, corrects your request, and promptly suggests to you that "Camptown Races" is the cause of your irritation. You confirm the computer's suggestion by listening to one of the many MP3 files it has found. Satisfied, you kindly decline the offer to retrieve all extant versions of the song, including a recently released Italian rap rendition and an orchestral score featuring a bagpipe duet. Does such a system exist today? No. Will it in the future? Yes. Will such a system be easy to produce? Most decidedly not. Myriad difficulties remain to be overcome before the creation, deployment, and evaluation of robust, large-scale, and content-based Music Information Retrieval (MIR) systems become reality. The dizzyingly complex interaction of music's pitch, temporal, harmonic, timbral, editorial, textual, and bibliographic "facets," for example, demonstrates just one of MIR's perplexing problems. The choice of music representation-whether symbol-based, audio-based, or both-further compounds matters, as each choice determines bandwidth, computation, storage, retrieval, and interface requirements and capabilities.

Source

Annual review of information science and technology. 37(2003), S.295-342

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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

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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

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Source

Annual review of information science and technology. 42(2008), S.xxx-xxx

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Source

Annual review of information science and technology. 40(2006), S.xxx-xxx

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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

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.13, S.1915-1933

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Source

Information processing and management. 36(2000) no.1, S.155-205

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Date

13. 7.2008 19:22:28

Source

Annual review of information science and technology. 40(2006), S.xxx-xxx

Theme

Information

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Abstract

An online series of articles explaining controlled vocabularies and, in particular, faceted classification. It is not yet finished, but what they have covered is very well done, practical and informative, with useful advice and a full treatment. It is worth reading now, and when they actually get to performing facet analysis and making a faceted system, it will make a very useful reference.

Theme

Klassifikationssysteme im Online-Retrieval
Verbale Doksprachen im Online-Retrieval

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Abstract

Information retrieval - the retrieval, primarily, of documents or textual material - is fundamentally a linguistic process. At the very least we must describe what we want and match that description with descriptions of the information that is available to us. Furthermore, when we describe what we want, we must mean something by that description. This is a deceptively simple act, but such linguistic events have been the grist for philosophical analysis since Aristotle. Although there are complexities involved in referring to authors, document types, or other categories of information retrieval context, here I wish to focus an one of the most problematic activities in information retrieval: the description of the intellectual content of information items. And even though I take information retrieval to involve the description and retrieval of written text, what I say here is applicable to any information item whose intellectual content can be described for retrieval-books, documents, images, audio clips, video clips, scientific specimens, engineering schematics, and so forth. For convenience, though, I will refer only to the description and retrieval of documents. The description of intellectual content can go wrong in many obvious ways. We may describe what we want incorrectly; we may describe it correctly but in such general terms that its description is useless for retrieval; or we may describe what we want correctly, but misinterpret the descriptions of available information, and thereby match our description of what we want incorrectly. From a linguistic point of view, we can be misunderstood in the process of retrieval in many ways. Because the philosophy of language deals specifically with how we are understood and mis-understood, it should have some use for understanding the process of description in information retrieval. First, however, let us examine more closely the kinds of misunderstandings that can occur in information retrieval. We use language in searching for information in two principal ways. We use it to describe what we want and to discriminate what we want from other information that is available to us but that we do not want. Description and discrimination together articulate the goals of the information search process; they also delineate the two principal ways in which language can fail us in this process. Van Rijsbergen (1979) was the first to make this distinction, calling them "representation" and "discrimination.""

Source

Annual review of information science and technology. 37(2003), S.3-50

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Abstract

This chapter examines the nature of semantic relations and their main applications in information science. The nature and types of semantic relations are discussed from the perspectives of linguistics and psychology. An overview of the semantic relations used in knowledge structures such as thesauri and ontologies is provided, as well as the main techniques used in the automatic extraction of semantic relations from text. The chapter then reviews the use of semantic relations in information extraction, information retrieval, question-answering, and automatic text summarization applications. Concepts and relations are the foundation of knowledge and thought. When we look at the world, we perceive not a mass of colors but objects to which we automatically assign category labels. Our perceptual system automatically segments the world into concepts and categories. Concepts are the building blocks of knowledge; relations act as the cement that links concepts into knowledge structures. We spend much of our lives identifying regular associations and relations between objects, events, and processes so that the world has an understandable structure and predictability. Our lives and work depend on the accuracy and richness of this knowledge structure and its web of relations. Relations are needed for reasoning and inferencing. Chaffin and Herrmann (1988b, p. 290) noted that "relations between ideas have long been viewed as basic to thought, language, comprehension, and memory." Aristotle's Metaphysics (Aristotle, 1961; McKeon, expounded on several types of relations. The majority of the 30 entries in a section of the Metaphysics known today as the Philosophical Lexicon referred to relations and attributes, including cause, part-whole, same and opposite, quality (i.e., attribute) and kind-of, and defined different types of each relation. Hume (1955) pointed out that there is a connection between successive ideas in our minds, even in our dreams, and that the introduction of an idea in our mind automatically recalls an associated idea. He argued that all the objects of human reasoning are divided into relations of ideas and matters of fact and that factual reasoning is founded on the cause-effect relation. His Treatise of Human Nature identified seven kinds of relations: resemblance, identity, relations of time and place, proportion in quantity or number, degrees in quality, contrariety, and causation. Mill (1974, pp. 989-1004) discoursed on several types of relations, claiming that all things are either feelings, substances, or attributes, and that attributes can be a quality (which belongs to one object) or a relation to other objects.
Linguists in the structuralist tradition (e.g., Lyons, 1977; Saussure, 1959) have asserted that concepts cannot be defined on their own but only in relation to other concepts. Semantic relations appear to reflect a logical structure in the fundamental nature of thought (Caplan & Herrmann, 1993). Green, Bean, and Myaeng (2002) noted that semantic relations play a critical role in how we represent knowledge psychologically, linguistically, and computationally, and that many systems of knowledge representation start with a basic distinction between entities and relations. Green (2001, p. 3) said that "relationships are involved as we combine simple entities to form more complex entities, as we compare entities, as we group entities, as one entity performs a process on another entity, and so forth. Indeed, many things that we might initially regard as basic and elemental are revealed upon further examination to involve internal structure, or in other words, internal relationships." Concepts and relations are often expressed in language and text. Language is used not just for communicating concepts and relations, but also for representing, storing, and reasoning with concepts and relations. We shall examine the nature of semantic relations from a linguistic and psychological perspective, with an emphasis on relations expressed in text. The usefulness of semantic relations in information science, especially in ontology construction, information extraction, information retrieval, question-answering, and text summarization is discussed. Research and development in information science have focused on concepts and terms, but the focus will increasingly shift to the identification, processing, and management of relations to achieve greater effectiveness and refinement in information science techniques. Previous chapters in ARIST on natural language processing (Chowdhury, 2003), text mining (Trybula, 1999), information retrieval and the philosophy of language (Blair, 2003), and query expansion (Efthimiadis, 1996) provide a background for this discussion, as semantic relations are an important part of these applications.

Source

Annual review of information science and technology. 40(2006), S.157-228

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Abstract

This is a classified, annotated bibliography about how to design faceted classification systems and make them usable on the World Wide Web. It is the first of three works I will be doing. The second, based on the material here and elsewhere, will discuss how to actually make the faceted system and put it online. The third will be a report of how I did just that, what worked, what didn't, and what I learned. Almost every article or book listed here begins with an explanation of what a faceted classification system is, so I won't (but see Steckel in Background below if you don't already know). They all agree that faceted systems are very appropriate for the web. Even pre-web articles (such as Duncan's in Background, below) assert that hypertext and facets will go together well. Combined, it is possible to take a set of documents and classify them or apply subject headings to describe what they are about, then build a navigational structure so that any user, no matter how he or she approaches the material, no matter what his or her goals, can move and search in a way that makes sense to them, but still get to the same useful results as someone else following a different path to the same goal. There is no one way that everyone will always use when looking for information. The more flexible the organization of the information, the more accommodating it is. Facets are more flexible for hypertext browsing than any enumerative or hierarchical system.
Consider movie listings in newspapers. Most Canadian newspapers list movie showtimes in two large blocks, for the two major theatre chains. The listings are ordered by region (in large cities), then theatre, then movie, and finally by showtime. Anyone wondering where and when a particular movie is playing must scan the complete listings. Determining what movies are playing in the next half hour is very difficult. When movie listings went onto the web, most sites used a simple faceted organization, always with movie name and theatre, and perhaps with region or neighbourhood (thankfully, theatre chains were left out). They make it easy to pick a theatre and see what movies are playing there, or to pick a movie and see what theatres are showing it. To complete the system, the sites should allow users to browse by neighbourhood and showtime, and to order the results in any way they desired. Thus could people easily find answers to such questions as, "Where is the new James Bond movie playing?" "What's showing at the Roxy tonight?" "I'm going to be out in in Little Finland this afternoon with three hours to kill starting at 2 ... is anything interesting playing?" A hypertext, faceted classification system makes more useful information more easily available to the user. Reading the books and articles below in chronological order will show a certain progression: suggestions that faceting and hypertext might work well, confidence that facets would work well if only someone would make such a system, and finally the beginning of serious work on actually designing, building, and testing faceted web sites. There is a solid basis of how to make faceted classifications (see Vickery in Recommended), but their application online is just starting. Work on XFML (see Van Dijck's work in Recommended) the Exchangeable Faceted Metadata Language, will make this easier. If it follows previous patterns, parts of the Internet community will embrace the idea and make open source software available for others to reuse. It will be particularly beneficial if professionals in both information studies and computer science can work together to build working systems, standards, and code. Each can benefit from the other's expertise in what can be a very complicated and technical area. One particularly nice thing about this area of research is that people interested in combining facets and the web often have web sites where they post their writings.
This bibliography is not meant to be exhaustive, but unfortunately it is not as complete as I wanted. Some books and articles are not be included, but they may be used in my future work. (These include two books and one article by B.C. Vickery: Faceted Classification Schemes (New Brunswick, NJ: Rutgers, 1966), Classification and Indexing in Science, 3rd ed. (London: Butterworths, 1975), and "Knowledge Representation: A Brief Review" (Journal of Documentation 42 no. 3 (September 1986): 145-159; and A.C. Foskett's "The Future of Faceted Classification" in The Future of Classification, edited by Rita Marcella and Arthur Maltby (Aldershot, England: Gower, 2000): 69-80). Nevertheless, I hope this bibliography will be useful for those both new to or familiar with faceted hypertext systems. Some very basic resources are listed, as well as some very advanced ones. Some example web sites are mentioned, but there is no detailed technical discussion of any software. The user interface to any web site is extremely important, and this is briefly mentioned in two or three places (for example the discussion of lawforwa.org (see Example Web Sites)). The larger question of how to display information graphically and with hypertext is outside the scope of this bibliography. There are five sections: Recommended, Background, Not Relevant, Example Web Sites, and Mailing Lists. Background material is either introductory, advanced, or of peripheral interest, and can be read after the Recommended resources if the reader wants to know more. The Not Relevant category contains articles that may appear in bibliographies but are not relevant for my purposes.

Theme

Klassifikationssysteme im Online-Retrieval

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Date

13. 7.2008 9:30:22

Source

Annual review of information science and technology. 42(2008), S.xxx-xxx

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Date

13. 7.2008 19:53:22

Source

Annual review of information science and technology. 41(2007), S.xxx-xxx

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Abstract

This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.

Source

Annual review of information science and technology. 39(2005), S.3-32

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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

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Abstract

The rationale for using information systems is to find information that helps us in our daily activities, be they tasks or interests. Systems are expected to support us in searching for and identifying useful information. Although the activities and tasks performed by humans generate information needs and searching, they have attracted little attention in studies of information searching. Such studies have concentrated an search tasks rather than the activities that trigger them. It is obvious that our understanding of information searching is only partial, if we are not able to connect aspects of searching to the related task. The expected contribution of information to the task is reflected in relevance assessments of the information items found, and in the search tactics and use of the system in general. Taking the task into account seems to be a necessary condition for understanding and explaining information searching, and, by extension, for effective systems design.

Source

Annual review of information science and technology. 37(2003), S.413-464

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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

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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