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Synthetische Dokumente - umfassende und kompakte Antworten auf Ihre Suchanfrage

Zielsetzung des Projekts ist die Entwicklung von Methoden und einem Prototypen einer Suchmaschine, die umfassende synthetische Dokumente als Antwort auf Benutzeranfragen liefert. Für eine Suchanfrage soll der Benutzer umfassende und gut organisierte Ergebnisse bekommen, die in einer kompakten und verständlichen Form dargestellt werden. Diese synthetischen Ergebnisdokumente werden aus semantisch organisierten und kategorisierten Informationsclustern zusammengesetzt, die nicht nur Informationen für die verschiedenen Kontexte der Anfrage liefern, sondern auch Erklärungen für Verbindungen zwischen ihnen. Die Struktur und Darstellung der Informationen wird in den einzelnen synthetischen Dokumenten an den Nutzer angepasst, um ihm eine personalisierte Darstellung zu bieten.

Laufzeit

  • August 2009 bis Juli 2010

Geldgeber

  • Hewlett Packard Labs Innovation Research Programme

Partner

  • TBD

Prof. Dr. Steffen Staab

B 108
+49 261 287-2761
staab@uni-koblenz.de

Short CV

I have studied computer science and computational linguistics at the Universität Erlangen-Nürnberg and at the University of Pennsylvania. I worked in the previous computational linguistics research group at the Universität Freiburg and did my Ph.D. in computer science in the faculty for technology in 1998. Afterwards I joined Universität Stuttgart, Institute IAT & Fraunhofer IAO, before I moved on to the Universität Karlsruhe (now: KIT), where I progressed from project lead, over lecturer and senior lecturer and did my habilitation in 2002. In 2004 I became professor for databases and information systems at Universität Koblenz-Landau, where I founded the Institute for Web Science and Technologies (WeST) in 2009. In parallel, I hold a Chair for Web and Computer Science at University of Southampton since March 2015.

Research Interests

Data represent the world on our computers. While the world is very intriguing, data may be quite boring, if one does not know what they mean. I am interested in making data more meaningful to find interesting insights in the world outside.

How does meaning arise?

  • One can model data and information. Conceptual models and ontologies are the foundations for knowledge networks that enable the computer to treat data in a meaningful way.
  • Text and data mining as well as information extraction find meaningful patterns in data (e.g. using ontology learning of text clustering) as well as connections between data and its use in context (e.g. using smartphones). Hence, knowledge networks may be found in data.
  • Humans communicate information. In order to understand what data and information means, one has to understand social interactions. In the context of social network knowledge networks become meaningful for human consumption.
  • Eventually meaning is nothing that exists in the void. Data and information must be communicated to people who may use insights into data and information. Interaction between humans and computers must happen in a way that matches the meaning of data and information.

The World Wide Web is the largest information construct made by mankind to convey meaningful data. Web Science is the discipline that considers how networks of people and knowledge in the Web arise, how humans deal with it and which consequences this has for all of us. The Web is a meaning machine that I want do understand by my research.

Where else you might find me?

In my office (room B110), traveling, running in the local forest or in Changa or at AHS. Watch out! 

Termination date: 
July, 2010