3 resultados para Functional Requirements for Authority Data (FRAD)

em SerWisS - Server für Wissenschaftliche Schriften der Fachhochschule Hannover


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Das Ziel dieser Arbeit ist es, ein Konzept für eine Darstellung der Personennamendatei(PND) in den Sprachen Resource Description Framework (RDF), Resource DescriptionFramework Schema Language (RDFS) und Web Ontology Language (OWL) zu entwickeln. Der Prämisse des Semantic Web folgend, Daten sowohl in menschenverständlicher als auch in maschinell verarbeitbarer Form darzustellen und abzulegen, wird eine Struktur für Personendaten geschaffen. Dabei wird von der bestehenden Daten- und Struktursituation im Pica-Format ausgegangen. Die Erweiterbarkeit und Anpassbarkeit des Modells im Hinblick auf zukünftige, im Moment gegebenenfalls noch nicht absehbare Anwendungen und Strukurveränderungen, muss aber darüber hinaus gewährleistet sein. Die Modellierung orientiert sich an bestehenden Standards wie Dublin Core, Friend Of A Friend (FOAF), Functional Requirements for Bibliographic Records (FRBR), Functional Requirements for Authority Data (FRAD) und Resource Description and Access (RDA).

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The use of secondary data in health care research has become a very important issue over the past few years. Data from the treatment context are being used for evaluation of medical data for external quality assurance, as well as to answer medical questions in the form of registers and research databases. Additionally, the establishment of electronic clinical systems like data warehouses provides new opportunities for the secondary use of clinical data. Because health data is among the most sensitive information about an individual, the data must be safeguarded from disclosure.

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The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.