Document (#39285)

Author
Engel, F.
Title
Expertensuche in semantisch integrierten Datenbeständen
Imprint
Hagen : Fernuniversität
Year
2015
Pages
XVI, 189 S
Abstract
Wissen ist das intellektuelle Kapital eines Unternehmens und der effektive Zugriff darauf entscheidend für die Anpassungsfähigkeit und Innovationskraft. Eine häufig angewandte Lösung für den erfolgreichen Zugriff auf diese Wissensressourcen ist die Umsetzung der Expertensuche in den Daten der verteilten Informationssysteme des Unternehmens. Aktuelle Expertensuchverfahren berücksichtigen zur Berechnung der Relevanz eines Kandidaten zumeist nur die Information aus Datenquellen (u. a. E-Mails oder Publikationen eines Kandidaten), über die eine Verbindung zwischen dem Thema der Frage und einem Kandidaten hergestellt werden kann. Die aus den Datenquellen gewonnene Information, fließt dann gewichtet in die Relevanzbewertung ein. Analysen aus dem Fachbereich Wissensmanagement zeigen jedoch, dass neben dem Themenbezug auch noch weitere Kriterien Einfluss auf die Auswahl eines Experten in einer Expertensuche haben können (u. a. der Bekanntheitsgrad zwischen dem Suchenden und Kandidat). Um eine optimale Gewichtung der unterschiedlichen Bestandteile und Quellen, aus denen sich die Berechnung der Relevanz speist, zu finden, werden in aktuellen Anwendungen zur Suche nach Dokumenten oder zur Suche im Web verschiedene Verfahren aus dem Umfeld des maschinellen Lernens eingesetzt. Jedoch existieren derzeit nur sehr wenige Arbeiten zur Beantwortung der Frage, wie gut sich diese Verfahren eignen um auch in der Expertensuche verschiedene Bestandteile der Relevanzbestimmung optimal zusammenzuführen. Informationssysteme eines Unternehmens können komplex sein und auf einer verteilten Datenhaltung basieren. Zunehmend finden Technologien aus dem Umfeld des Semantic Web Akzeptanz in Unternehmen, um eine einheitliche Zugriffsschnittstelle auf den verteilten Datenbestand zu gewährleisten. Der Zugriff auf eine derartige Zugriffschnittstelle erfolgt dabei über Abfragesprachen, welche lediglich eine alphanumerische Sortierung der Rückgabe erlauben, jedoch keinen Rückschluss auf die Relevanz der gefundenen Objekte zulassen. Für die Suche nach Experten in einem derartig aufbereiteten Datenbestand bedarf es zusätzlicher Berechnungsverfahren, die einen Rückschluss auf den Relevanzwert eines Kandidaten ermöglichen. In dieser Arbeit soll zum einen ein Beitrag geleistet werden, der die Anwendbarkeit lernender Verfahren zur effektiven Aggregation unterschiedlicher Kriterien in der Suche nach Experten zeigt. Zum anderen soll in dieser Arbeit nach Möglichkeiten geforscht werden, wie die Relevanz eines Kandidaten über Zugriffsschnittstellen berechnet werden kann, die auf Technologien aus dem Umfeld des Semantic Web basieren.
Content
Dissertation zur Erlangung des akademischen Grades Doktor-Ingenieur (Dr.-Ing.) der Fakultät für Mathematik und Informatik der FernUniversität in Hagen. Vgl.: http://deposit.fernuni-hagen.de/3042/.

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