Context-adaption based on ontologies and spreading activation

Hussein, T., Westheide, D., & Ziegler, J. (2007). In A. Hinneburg (Ed.), LWA 2007: Lernen – Wissen – Adaption. Martin-Luther-Universität Halle-Wittenberg.

Abstract

Ontologies and spreading activation are known terms within the scope of information retrieval. In this paper we introduce SPREADR, an integrated adaptation mechanism for web applications that uses ontologies for representing the application domain as well as context information like location, user history and local time. Those context factors can be modeled in an ontology and be linked to certain domain nodes. In each session a Spreading Activation Network is build based on those ontologies and recognized context factors or user actions can trigger an activation flow through this network. A node’s resulting activation value then represents its importance according to the current circumstances. While identically in structure, the Spreading Activation Networks are personalized by automatically modifying link weights and activation levels of nodes. As a result the system learns about the user preferences and can adjust its adaptation mechanism for future runs through implicit feedback.

Resources

Related publications

Adapting web sites by spreading activation in ontologies

Hussein, T., & Ziegler, J. (2008). In ReColl ’08: Int. Workshop on Recommendation and Collaboration (in conjunction with IUI 2008). Gran Canaria.

Situationsgerechtes Recommending – Kontextadaptive, hybride Empfehlungsgenerierung

Hussein, T., & Ziegler, J. (2011). Informatik Spektrum, 34(2), 143–152.

Context Modeling for Adaptive Collaboration

Haake, J., Hussein, T., Joop, B., Lukosch, S., Veiel, D., & Ziegler, J. (2009). Essen: Abteilung für Informatik und Angewandte Kognitionswissenschaft, Fakultät für Ingenieurwissenschaften, Universität Duisburg-Essen.

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Research project

CONTici

Context-adaptive interaction in cooperative knowledge processes

Research development

SPREADR

Spreading Activation Driven Reasoning

Focus area