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Guided curation of semistructured data in collaboratively-built knowledge bases
Affiliation:1. University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC;2. Division of Urology, Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA;3. Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, NC;4. University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, NC;5. Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, NC;1. Assistant Professor, Department of Developmental Sciences/Orthodontics, Marquette University School of Dentistry, Milwaukee, Wisconsin.;2. Assistant Professor, Division of Orthodontics, University of Connecticut Health, Farmington, Connecticut;3. Associate Professor, Division of Orthodontics, University of Alberta, Alberta, Ontario, Canada;4. Associate Professor, Division of Orthodontics, Program Director, Orthodontic Fellowship Program, University of Connecticut Health, Farmington, Connecticut;5. Associate Professor, Division of Oral and Maxillofacial Radiology, University of Connecticut Health, Farmington, Connecticut;6. Associate Professor, Division of Orthodontics, University of Connecticut Health, Farmington, Connecticut
Abstract:The collaborative curation of semistructured knowledge has become a popular paradigm on the web and also within enterprises. In such knowledge bases a common structure of the stored information is crucial for providing efficient and precise search facilities. However, the task of refining, extending and homogenizing knowledge and its structure is very complex. In this article we present two paradigms for the simplification of this task by providing guidance mechanisms to the user. Both paradigms aim at combining the power of automated extraction algorithms with the semantic awareness of human users to accomplish this refinement task.
Keywords:Collaboration  Semistructured data  RDF  Recommendations  Schema proliferation  Mixed-initiative
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