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Date

22. 6.2015 16:08:38

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Date

22. 2.2018 12:06:44
22. 2.2018 12:13:53

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Date

22. 2.2013 12:35:05

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Date

26. 8.2015 10:22:00

Source

Praxishandbuch Bibliotheksmanagement. Hrsg.: R. Griebel, u.a. Bd.1

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Date

15. 1.2016 19:22:28

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Date

14. 2.2012 12:53:22

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Content

Vortrag auf der Tagung "Geschichte und Modellierung", Jena, 3. Februar 2012. Vgl. unter: http://www.fmi.uni-jena.de/Fakult%C3%A4t/Institute+und+Abteilungen/Abteilung+f%C3%BCr+Didaktik/Kolloquien.html?highlight=neunzert.

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Abstract

This paper aims to study the conceptual dimension of Knowledge Organization (KO) in the ISKO proceedings (1990-2012) domain. After analyzing a corpus of 71 papers that presented the term "knowledge organization" in their titles, using the methodology of Bardin's content analysis, it was possible to obtain a set of 11 definitions of KO which were studied using the following categories: nature, object, tools, processes, and perspectives/approaches. These categories act as a basis to identify the communities of authors that interact in the domain under different conceptual perspectives. The results show that KO has been mainly understood as an area or field of knowledge whose objects are recorded knowledge and conceptual structures, and whose main processes are classification and indexing, as well as information retrieval. The nature of KO is mostly linked to the construction of specialized discourses and the methodological dimension of such area is related to the systematization of recorded scientific knowledge.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Content

Vgl. unter: https://books.google.de/books?hl=de&lr=&id=0rtGDwAAQBAJ&oi=fnd&pg=PA35&dq=inhaltserschlie%C3%9Fung+OR+sacherschlie%C3%9Fung&ots=5u0TwCbFqE&sig=GGw3Coc21CINkone-6Lx8LaSAjY#v=onepage&q=inhaltserschlie%C3%9Fung%20OR%20sacherschlie%C3%9Fung&f=false.

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Date

13.12.2017 14:17:22

Type

r

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Date

1. 2.2016 18:25:22

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Source

Dialog mit Bibliotheken. 22(2010) H.1, S.20-22

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Abstract

Purpose The introduction of "altmetrics" as new tools to analyze scientific impact within the reward system of science has challenged the hegemony of citations as the predominant source for measuring scientific impact. Mendeley readership has been identified as one of the most important altmetric sources, with several features that are similar to citations. The purpose of this paper is to perform an in-depth analysis of the differences and similarities between the distributions of Mendeley readership and citations across fields. Design/methodology/approach The authors analyze two issues by using in each case a common analytical framework for both metrics: the shape of the distributions of readership and citations, and the field normalization problem generated by differences in citation and readership practices across fields. In the first issue the authors use the characteristic scores and scales method, and in the second the measurement framework introduced in Crespo et al. (2013). Findings There are three main results. First, the citations and Mendeley readership distributions exhibit a strikingly similar degree of skewness in all fields. Second, the results on "exchange rates (ERs)" for Mendeley readership empirically supports the possibility of comparing readership counts across fields, as well as the field normalization of readership distributions using ERs as normalization factors. Third, field normalization using field mean readerships as normalization factors leads to comparably good results. Originality/value These findings open up challenging new questions, particularly regarding the possibility of obtaining conflicting results from field normalized citation and Mendeley readership indicators; this suggests the need for better determining the role of the two metrics in capturing scientific recognition.

Date

20. 1.2015 18:30:22

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Abstract

Purpose - The growing volumes of semantic data available in the web result in the need for handling the information overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and use this data, especially for lay-users without experience with Semantic Web technologies. The paper aims to discuss these issues. Design/methodology/approach - The Visual Information-Seeking Mantra "Overview first, zoom and filter, then details-on-demand" proposed by Shneiderman describes how data should be presented in different stages to achieve an effective exploration. The overview is the first user task when dealing with a data set. The objective is that the user is capable of getting an idea about the overall structure of the data set. Different information architecture (IA) components supporting the overview tasks have been developed, so they are automatically generated from semantic data, and evaluated with end-users. Findings - The chosen IA components are well known to web users, as they are present in most web pages: navigation bars, site maps and site indexes. The authors complement them with Treemaps, a visualization technique for displaying hierarchical data. These components have been developed following an iterative User-Centered Design methodology. Evaluations with end-users have shown that they get easily used to them despite the fact that they are generated automatically from structured data, without requiring knowledge about the underlying semantic technologies, and that the different overview components complement each other as they focus on different information search needs. Originality/value - Obtaining semantic data sets overviews cannot be easily done with the current semantic web browsers. Overviews become difficult to achieve with large heterogeneous data sets, which is typical in the Semantic Web, because traditional IA techniques do not easily scale to large data sets. There is little or no support to obtain overview information quickly and easily at the beginning of the exploration of a new data set. This can be a serious limitation when exploring a data set for the first time, especially for lay-users. The proposal is to reuse and adapt existing IA components to provide this overview to users and show that they can be generated automatically from the thesaurus and ontologies that structure semantic data while providing a comparable user experience to traditional web sites.

Date

20. 1.2015 18:30:22

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Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Abstract

The object of this literature review is to provide a historical perspective of R and D work in the area of Knowledge Organization (KO). This overview/summarization will provide information on major areas of KO. Journal articles published in core areas of KO: (Classification, Indexing, Thesauri and Taxonomies, Internet and Subject approach to information in the electronic era and Ontologies will be predominantly covered in this literature review. Coverage in this overview may not be completely exhaustive, but it succinctly showcases major developments in the area of KO. This review is a good source of additional reading material on KO apart from prescribed reading material on KO

Date

22. 6.2015 16:13:38

Type

r

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Source

BuB. 63(2011) H.1, S.22-23

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Date

22. 8.2014 17:05:18

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Date

13. 9.2014 14:45:22

Type

r

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Abstract

What is knowledge representation and reasoning (KR&R)? Alas, a thorough account would require a book, or at least a dedicated, full-length paper, but here we shall have to make do with something simpler. Since most readers are likely to have an intuitive grasp of the essence of KR&R, our simple account should suffice. The interesting thing is that this simple account itself makes reference to some of the foundational distinctions in the field of philosophy. These distinctions also play a central role in artificial intelligence (AI) and computer science. To begin with, the first distinction in KR&R is that we identify knowledge with knowledge that such-and-such holds (possibly to a degree), rather than knowing how. If you ask an expert tennis player how he manages to serve a ball at 130 miles per hour on his first serve, and then serve a safer, topspin serve on his second should the first be out, you may well receive a confession that, if truth be told, this athlete can't really tell you. He just does it; he does something he has been doing since his youth. Yet, there is no denying that he knows how to serve. In contrast, the knowledge in KR&R must be expressible in declarative statements. For example, our tennis player knows that if his first serve lands outside the service box, it's not in play. He thus knows a proposition, conditional in form.

Date

9. 2.2017 19:22:14

Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss