Search (11 results, page 1 of 1)

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Date

23. 1.2022 10:22:18

Type

a

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Language

a

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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.
Although the Alexandria Digital Library provides far richer data than the TGN (5.9 vs. 1.3 million names), its added size lowers, rather than increases, the accuracy of most geographic name identification systems for historical documents: most of the extra 4.6 million names cover low frequency entities that rarely occur in any particular corpus. The TGN is sufficiently comprehensive to provide quite enough noise: we find place names that are used over and over (there are almost one hundred Washingtons) and semantically ambiguous (e.g., is Washington a person or a place?). Comprehensive knowledge sources emphasize recall but lower precision. We need data with which to determine which "Tribune" or "John Brown" a particular passage denotes. Secondly and paradoxically, our reference works may not be comprehensive enough. Human actors come and go over time. Organizations appear and vanish. Even places can change their names or vanish. The TGN does associate the obsolete name Siam with the nation of Thailand (tgn,1000142) - but also with towns named Siam in Iowa (tgn,2035651), Tennessee (tgn,2101519), and Ohio (tgn,2662003). Prussia appears but as a general region (tgn,7016786), with no indication when or if it was a sovereign nation. And if places do point to the same object over time, that object may have very different significance over time: in the foundational works of Western historiography, Herodotus reminds us that the great cities of the past may be small today, and the small cities of today great tomorrow (Hdt. 1.5), while Thucydides stresses that we cannot estimate the past significance of a place by its appearance today (Thuc. 1.10). In other words, we need to know the population figures for the various Washingtons in 1870 if we are analyzing documents from 1870. The foundations have been laid for reference works that provide machine actionable information about entities at particular times in history. The Alexandria Digital Library Gazetteer Content Standard8 represents a sophisticated framework with which to create such resources: places can be associated with temporal information about their foundation (e.g., Washington, DC, founded on 16 July 1790), changes in names for the same location (e.g., Saint Petersburg to Leningrad and back again), population figures at various times and similar historically contingent data. But if we have the software and the data structures, we do not yet have substantial amounts of historical content such as plentiful digital gazetteers, encyclopedias, lexica, grammars and other reference works to illustrate many periods and, even if we do, those resources may not be in a useful form: raw OCR output of a complex lexicon or gazetteer may have so many errors and have captured so little of the underlying structure that the digital resource is useless as a knowledge base. Put another way, human beings are still much better at reading and interpreting the contents of page images than machines. While people, places, and dates are probably the most important core entities, we will find a growing set of objects that we need to identify and track across collections, and each of these categories of objects will require its own knowledge sources. The following section enumerates and briefly describes some existing categories of documents that we need to mine for knowledge. This brief survey focuses on the format of print sources (e.g., highly structured textual "database" vs. unstructured text) to illustrate some of the challenges involved in converting our published knowledge into semantically annotated, machine actionable form.

Type

a

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Abstract

I have become "brutally honest" of late, at least according to one listener who heard my remarks during a recent whistle stop speaking tour of publishing conventions. This comment caught me a little off guard. Not that I haven't always been frank, but I do try never to be brutal. The truth, I guess, can be painful, even if the intention of the teller is simply objectivity. This paper is based on a "brutally honest" talk I have been giving to publishers, first, in February, to the Association of American Publishers' Professional and Scholarly Publishing Division, at which point I was calling the piece, "The Illusion of Free Information." It was this initial rendition that led to the invitation to publish something here. Since then I've been working on the talk. I gave a second version of it in March to the assembly of the American Society of Information Dissemination Centers, where I called it, "When Sectors Clash: Public Access vs. Private Interest." And, most recently, I gave yet a third version of it to the governing board of the American Institute of Physics. This time I called it: "The Future of Society Publishing." The notion of free information, our government's proper role in distributing free information, and the future of scholarly publishing in a world of free information . . . these are the issues that are floating around in my head. My goal here is to tell you where my thinking is only at this moment, for I reserve the right to continue thinking and developing new permutations on this mentally challenging theme.

Type

a

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Abstract

A publication on the foundation of knowledge organization

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Abstract

In New Scientist issue 18 Sept 1999, "Life force" pp27-30 Paul Davies writes "an apparently random sequence such as 110101001010010111... cannot be condensed into a simple set of instructions, so it has a high information content." (p29). This notion of 'information content' leads to paradoxes. Consider random number generator software. Let it generate 100 and 1000 random numbers. According to the above definition the second sequence of numbers has an information content ten times higher than the first, because its description would be ten times longer. However they are both generated by the same simple set of instructions, so should have exactly the same 'information content'. There is the paradox. It seems clear that this measure of 'information content' misses the point. It measures compressibility of a sequence, not 'information content'. One needs meaning of a sequence to capture information content.

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Abstract

The focus of this thesis is information use. Although a key concept in information behaviour, information use has received little attention from information science researchers. Studies of other key concepts such as information need and information seeking are dominant in information behaviour research. Information use is an area of interest to information professionals who rely on research outcomes to shape their practice. There are few empirical studies of how people actually use information that might guide and refine the development of information systems, products and services.

Content

A thesis submitted to the University of Technology, Sydney in fulfilment of the requirements for the degree of Doctor of Philosophy. - Vgl. unter: http://epress.lib.uts.edu.au/dspace/bitstream/2100/309/2/02whole.pdf.

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Abstract

Das bit als Maß für das Verhältnis von Information, informationstheoretischem Rauschen, a posteriori Redundanz, a priori Redundanz bzw. Wissen, so wie es sich zunächst in den Betrachtungen zur Entropie, aus der Thermodynamik heraus ergeben hat, ist ein weitaus fundamentaleres Maß, als es zunächst aus der Thermodynamik und dem Eta-Theorem Boltzmanns heraus zu erwarten war, weil es uns die Möglichkeit gibt, das Wissen von Lebewesen, und von Systemen mit Künstlicher Intelligenz zu messen und nicht mehr wie bisher nur vergleichend abzuschätzen. Es ist ein Maß für Ordnung bzw. Redundanz und hat nichts mit Energie zu tun. Im Sinne Galileis, das zu messen, was messbar ist, und messbar zu machen, was noch nicht messbar ist, wird die Messbarkeit von Wissen im 21. Jahrhundert, in dem die Wissenschaft der dominierende Faktor der menschlichen Gesellschaft ist, fundamentale Bedeutung erlangen. Das bit ist im Gegensatz zu den klassischen naturwissenschaftlichen Maßsystemen, wie Meter, Kilogramm, Sekunde oder Kelvin nicht mehr ein anthropozentrisches, also vom Menschen beliebig gewähltes Maß, es ist die Konsequenz grundlegender wissenschaftlicher Einsichten des letzten Jahrhunderts über die Bedeutung der Entropie.

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Type

a

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a

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Content

"Information Literacy lies at the core of lifelong learning. It empowers people in all walks of life to seek, evaluate, use and create information effectively to achieve their personal, social, occupational and educational goals. It is a basic human right in a digital world and promotes social inclusion of all nations. Lifelong learning enables individuals, communities and nations to attain their goals and to take advantage of emerging opportunities in the evolving global environment for shared benefit. It assists them and their institutions to meet technological, economic and social challenges, to redress disadvantage and to advance the well being of all.