Hybrid Knowledge Services for Knowledge Work Support
Published on 26. May 2014
Book
196 pages
978-3-902976-17-8 (ISBN)
Description
According to Peter Drucker, one of the major challenges of the 21st century is to increase the productivity of knowledge workers the way the productivity of industrial workers was increased in the 19th century. However, the dynamics and discretionary nature of knowledge work has made the design of useful and usable support mechanisms extremely difficult. We argue that in order to support knowledge work effectively, its inherent learning dimension must be taken into account. In this work, we define work-integrated learning (WIL) as work processes with learning as a by-product. We understand WIL as a work-embedded process in which (new) knowledge is acquired, created, applied, and transferred through the interactions between knowledge workers, knowledge artifacts, and knowledge structures. In practice, these knowledge entities and especially their relationships are rarely available explicitly. Our approach to supporting WIL is to design knowledge services for discovering mutual relationships between these knowledge entities and enhance them with new knowledge. Specifically, our hybrid approach fortifies coarse-grained semantic models with a battery of soft computing methods (such as machine learning), improved over time through usage data and user feedback (collective intelligence).
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Person
Professor Stefanie Lindstaedt began studying Computer Science at the Technical University Darmstadt (Germany). Before long she was awarded a stipend to attend the University of Colorado at Boulder (USA), where she completed her MS on Artificial Neural Networks and PhD on Organizational Memories. In 2010 Stefanie received her habilitation in Computer Science at Graz University of Technology (TUG) and in 2011 became the first female professor in Austria to lead a computer science institute (the Knowledge Management Institute at TUG) and a competence center (Know-Center, an Austrian research center for data-driven business). Stefanie's research focus is on reducing information complexity and context-aware information delivery based on combining various knowledge technologies with models of human cognition. Her goal is to develop context-aware knowledge services that support individual, community, and organizational learning by integrating collective intelligence approaches, semantic technologies and machine learning methods. Stefanie conducts her interdisciplinary research within the framework of EU-funded projects and numerous applied research projects with industry partners. Stefanie has over 150 scientific publications and supervised over 15 PhD and 20 Master's theses. She received many awards and is regularly invited as a speaker at conferences. Stefanie chairs the i-KNOW conference series (www.i-know.at), one of the most influential events on knowledge technologies in the German-speaking domain.