Context modeling and measuring for proactive resource recommendation in business collaboration

Authors:
Ruinan Gong;Ke Ning;Qing Li;David O'Sullivan;Yuliu Chen;Stefan Decker
Affiliations:
Department of Automation, Tsinghua University, Beijing 100084, China and Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Department of Automation, Tsinghua University, Beijing 100084, China;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland;Department of Automation, Tsinghua University, Beijing 100084, China;Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland
Venue:
Computers and Industrial Engineering
Year:
2009

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Abstract

Knowledge workers rely on collaboration with a large number of knowledge resources (people and machines) when performing tasks. Even highly experienced knowledge workers sometimes find it difficult to keep up to date with the number of resources available to help them complete a particular goal. This paper proposes a context-sensitive collaboration system to help improve the working efficiency of knowledge workers. The system provides recommendations to both information and human resources based on understanding their respective goals. The key to enabling context reasoning is an OWL-based context model and two metrics based on semantic aspects of the ontology to measure context granularity and association. The proposed solution is easy to implement and can benefit users by facilitating collaboration work and extended enterprises by providing access to expert knowledge.