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Date

20. 1.2015 18:30:22

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Date

14. 1.2020 11:15:22

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Abstract

Shannon provides a model whereby an information source selects a desired message, out of a set of possible messages, that is then formed into a signal. The signal is sent over the communication channel to a receiver, which then transforms the signal back to a message that is relayed to its destination (Shannon & Weaver, 1949/1963, p. 7). Problems connected with this model have remained with us. Some of the concepts are ambiguous; the identification of information with a process has spancelled the debate; the problems of measuring the amount of information, the relation of information to meaning, and questions about the truth value of information have remained. Balancing attention between the process and the act of receiving information, and deterrnining the character of the receiver, has also been the focus of work and debate. Information science has mined work from other disciplines involving information theory and has also produced its own theory. The desire for theory remains (Hjorland, 1998; Saracevic, 1999), but what theory will deliver is unclear. The distinction between data and information, or communication and information, is not of concern here. The convention that data, at some point of use, become information, and that information is transferred in a process of communication suffices for this discussion. Substitution of any of these terms is not a problem. More problematic is the relationship between information and knowledge. It seems accepted that at some point the data by perception, or selection, become information, which feeds and alters knowledge structures in a human recipient. What that process of alteration is, and its implications, remain problematic. This review considers the following questions: 1. What can be gleaned from the history of reviews of information in information science? 2. What current maps, guides, and surveys are available to elaborate our understanding of the issues? 3. Is there a parallel development of work outside information science an information theory of use to us? 4. Is there a dominant view of information within information science? 5. What can we say about issues like measurement, meaning, and misinformation? 6. Is there other current work of relevance that can assist attempts, in information science, to develop a theory of information?

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Abstract

A major contribution of cognitive psychology has been the conceptualization of knowledge as memory representations in the form of scripts, frames, or schemata (Anderson & Pearson, 1984; Rumelhart & Ortony, 1977; Shank & Abelson, 1977; Spiro, 1980). Schemata are defined as "packets of integrated information on various topics" (Hunt, 1993 , p.530). Throughout the 1970s and 1980s, cognitive psychologists were interested in describing the nature of these packets of information. Spiro (1980 ) demonstrated the constructive and complex nature of schemata and highlighted contextual factors--including tasks, texts, and situational contexts--that influenced how knowledge is organized in memory. Recently, cognitive researchers have come to view knowledge and schemata as multidimensional (Jetton, Rupley, & Willson, 1995). For example, researchers have differentiated novice and experts' knowledge structures in subject-matter domains (Chase & Simon, 1973; Chi, Glaser, & Rees, 1982; Larkin, McDermott, Simon, & Simon, 1981; Voss, Greene, Post, & Penner, 1983). Researchers have examined discourse knowledge--knowledge about language and how it works (McCutchen, 1986). Another aspect of knowledge that has been extensively studied is strategic knowledge--knowledge about procedures for accomplishing a goal or task (Alexander & Judy, 1988; J. R. Anderson, 1983a; Prawat, 1989).

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Date

5. 1.1997 9:39:22

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Date

16. 3.2008 12:22:08

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Abstract

Consider a sentence such as "the current price of tea in China is 35 cents per pound." In a library with millions of books we might find many statements of the above form that we could capture today with relatively simple rules: rather than pursuing every variation of a statement, programs can wait, like predators at a water hole, for their informational prey to reappear in a standard linguistic pattern. We can make inferences from sentences such as "NAME1 born at NAME2 in DATE" that NAME more likely than not represents a person and NAME a place and then convert the statement into a proposition about a person born at a given place and time. The changing price of tea in China, pedestrian birth and death dates, or other basic statements may not be truth and beauty in the Phaedrus, but a digital library that could plot the prices of various commodities in different markets over time, plot the various lifetimes of individuals, or extract and classify many events would be very useful. Services such as the Syllabus Finder1 and H-Bot2 (which Dan Cohen describes elsewhere in this issue of D-Lib) represent examples of information extraction already in use. H-Bot, in particular, builds on our evolving ability to extract information from very large corpora such as the billions of web pages available through the Google API. Aside from identifying higher order statements, however, users also want to search and browse named entities: they want to read about "C. P. E. Bach" rather than his father "Johann Sebastian" or about "Cambridge, Maryland", without hearing about "Cambridge, Massachusetts", Cambridge in the UK or any of the other Cambridges scattered around the world. Named entity identification is a well-established area with an ongoing literature. The Natural Language Processing Research Group at the University of Sheffield has developed its open source Generalized Architecture for Text Engineering (GATE) for years, while IBM's Unstructured Information Analysis and Search (UIMA) is "available as open source software to provide a common foundation for industry and academia." Powerful tools are thus freely available and more demanding users can draw upon published literature to develop their own systems. Major search engines such as Google and Yahoo also integrate increasingly sophisticated tools to categorize and identify places. The software resources are rich and expanding. The reference works on which these systems depend, however, are ill-suited for historical analysis. First, simple gazetteers and similar authority lists quickly grow too big for useful information extraction. They provide us with potential entities against which to match textual references, but existing electronic reference works assume that human readers can use their knowledge of geography and of the immediate context to pick the right Boston from the Bostons in the Getty Thesaurus of Geographic Names (TGN), but, with the crucial exception of geographic location, the TGN records do not provide any machine readable clues: we cannot tell which Bostons are large or small. If we are analyzing a document published in 1818, we cannot filter out those places that did not yet exist or that had different names: "Jefferson Davis" is not the name of a parish in Louisiana (tgn,2000880) or a county in Mississippi (tgn,2001118) until after the Civil War.

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Date

22. 6.2005 15:30:21