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Preface for the Third Edition - Read

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2. Knowledge organization:<br />

7. Systems 327<br />

development and management of knowledge maps: knowledge maps are not<br />

developed separately from <strong>the</strong> KMS, but <strong>the</strong> KMS provides functions that<br />

help <strong>the</strong> knowledge manager to semi-automatically derive maps from <strong>the</strong> contents<br />

of <strong>the</strong> KMS. Examples are InXight Smart Discovery (InXight), SemioMap<br />

(Semio Corp.), ThemeScape (Cartia) and AnswerGarden2 526 ,<br />

knowledge repository: a repository is a system used to store meta-data about<br />

objects of in<strong>for</strong>mation systems such as data, functions, application systems,<br />

hardware, users or organizational units (Mertens et al. 1997, 345f). Knowledge<br />

repositories support <strong>the</strong> management of meta-in<strong>for</strong>mation <strong>for</strong> knowledge<br />

elements (e.g., documents, authors, experts, communities),<br />

automatic indexing of full texts: documents are scanned with text mining techniques<br />

that suggest a list of keywords <strong>for</strong> <strong>the</strong> texts which is compatible to <strong>the</strong><br />

organization’s knowledge structure (Gro<strong>the</strong>/Gentsch 2000, 212ff),<br />

automatic integration/classification/linking of knowledge elements: again,<br />

text mining techniques are applied in order to e.g., discover interesting relationships<br />

between documents, classify documents, integrate <strong>the</strong>m with <strong>the</strong><br />

knowledge structure or cluster documents that cannot be integrated into <strong>the</strong><br />

organization’s knowledge structure. Thus, text mining provides techniques <strong>for</strong><br />

a bottom-up document-driven categorization of knowledge elements which<br />

can be combined with a top-down categorization developed in e.g., an expert<br />

workshop (Gro<strong>the</strong>/Gentsch 2000, 217),<br />

semantic analysis of knowledge elements: <strong>the</strong> KMS discovers relationships<br />

within and between knowledge elements. On <strong>the</strong> basis of techniques such as<br />

language analysis, semantic nets of terms are developed that describe a collection<br />

of knowledge elements,<br />

(hyper-)linking of published contents (within documents): traditional documents<br />

(e.g., developed with text processing software such as MS Word) are<br />

trans<strong>for</strong>med into hypertext documents in which hyperlinks are used to directly<br />

navigate within <strong>the</strong> documents, e.g., between sections of <strong>the</strong> documents or to<br />

cross-references,<br />

structuring and management of knowledge clusters: <strong>the</strong> KMS provides functions<br />

to support <strong>the</strong> development and management of <strong>the</strong>me-specific knowledge<br />

areas or clusters containing knowledge elements to a specific topic.<br />

7.4.5 Collaboration services<br />

Apart from <strong>the</strong> advanced management of knowledge elements as described in <strong>the</strong><br />

groups of services above 527 , communication and cooperation is <strong>the</strong> second impor-<br />

526. See also <strong>the</strong> function integrated presentation of knowledge elements in knowledge maps<br />

in section 7.4.3 - “Discovery services” on page 322.<br />

527. See sections 7.4.3 - “Discovery services” on page 322 until 7.4.4 - “Publication services”<br />

on page 326.

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