Document (#37898)

Author

Carevic, Z.

Title

Semi-automatische Verschlagwortung zur Integration externer semantischer Inhalte innerhalb einer medizinischen Kooperationsplattform

Imprint

Köln : Fachhochschule / Campus Gummersbach

Year

2012

Pages

99 S

Abstract

Die vorliegende Arbeit beschäftigt sich mit der Integration von externen semantischen Inhalten auf Basis eines medizinischen Begriffssystems. Die zugrundeliegende Annahme ist, dass die Verwendung einer einheitlichen Terminologie auf Seiten des Anfragesystems und der Wissensbasis zu qualitativ hochwertigen Ergebnissen führt. Um dies zu erreichen muss auf Seiten des Anfragesystems eine Abbildung natürlicher Sprache auf die verwendete Terminologie gewährleistet werden. Dies geschieht auf Basis einer (semi-)automatischen Verschlagwortung textbasierter Inhalte. Im Wesentlichen lassen sich folgende Fragestellungen festhalten: Automatische Verschlagwortung textbasierter Inhalte Kann eine automatische Verschlagwortung textbasierter Inhalte auf Basis eines Begriffssystems optimiert werden? Der zentrale Aspekt der vorliegenden Arbeit ist die (semi-)automatische Verschlagwortung textbasierter Inhalte auf Basis eines medizinischen Begriffssystems. Zu diesem Zweck wird der aktuelle Stand der Forschung betrachtet. Es werden eine Reihe von Tokenizern verglichen um zu erfahren welche Algorithmen sich zur Ermittlung von Wortgrenzen eignen. Speziell wird betrachtet, wie die Ermittlung von Wortgrenzen in einer domänenspezifischen Umgebung eingesetzt werden kann. Auf Basis von identifizierten Token in einem Text werden die Auswirkungen des Stemming und POS-Tagging auf die Gesamtmenge der zu analysierenden Inhalte beobachtet. Abschließend wird evaluiert wie ein kontrolliertes Vokabular die Präzision bei der Verschlagwortung erhöhen kann. Dies geschieht unter der Annahme dass domänenspezifische Inhalte auch innerhalb eines domänenspezifischen Begriffssystems definiert sind. Zu diesem Zweck wird ein allgemeines Prozessmodell entwickelt anhand dessen eine Verschlagwortung vorgenommen wird.
Integration externer Inhalte Inwieweit kann die Nutzung einer einheitlichen Terminologie zwischen Anfragesystem und Wissensbasis den Prozess der Informationsbeschaffung unterstützen? Zu diesem Zweck wird in einer ersten Phase ermittelt welche Wissensbasen aus der medizinischen Domäne in der Linked Data Cloud zur Verfügung stehen. Aufbauend auf den Ergebnissen werden Informationen aus verschiedenen dezentralen Wissensbasen exemplarisch integriert. Der Fokus der Betrachtung liegt dabei auf der verwendeten Terminologie sowie der Nutzung von Semantic Web Technologien. Neben Informationen aus der Linked Data Cloud erfolgt eine Suche nach medizinischer Literatur in PubMed. Wie auch in der Linked Data Cloud erfolgt die Integration unter Verwendung einer einheitlichen Terminologie. Eine weitere Fragestellung ist, wie Informationen aus insgesamt 21. Mio Aufsatzzitaten in PubMed sinnvoll integriert werden können. Dabei wird ermittelt welche Mechanismen eingesetzt werden können um die Präzision der Ergebnisse zu optimieren. Eignung medizinischer Begriffssystem Welche medizinischen Begriffssysteme existieren und wie eignen sich diese als zugrungeliegendes Vokabular für die automatische Verschlagwortung und Integration semantischer Inhalte? Der Fokus liegt dabei speziell auf einer Bewertung der Reichhaltigkeit von Begriffssystemen, wobei insbesondere der Detaillierungsgrad von Interesse ist. Handelt es sich um ein spezifisches oder allgemeines Begriffssystem und eignet sich dieses auch dafür bestimmte Teilaspekte der Medizin, wie bspw. die Chirurige oder die Anästhesie, in einer ausreichenden Tiefe zu beschreiben?

Content

Masterarbeit, ausgearbeitet von Zeljko Carevic zur Erlangung des akademischen Grades Master of Science vorgelegt an der Fachhochschule Köln Campus Gummersbach im Studiengang Medieninformatik. Vgl.. unter: http://opus.bibl.fh-koeln.de/volltexte/2012/376/pdf/Carevic.pdf.

Theme

Automatisches Indexieren

Field

Medizin

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5. Gödert, W.: Verbale Sacherschließung im Fach Mathematik (1979) 0.19

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