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EMP 2014 : First International Workshop - Empirical 2014

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Link: http://2014.empirical-ws.org
 
When May 26, 2014 - May 26, 2014
Where Crete, Greece
Submission Deadline Mar 6, 2014
Categories    semantic web
 

Call For Papers

The Empirical 2014 workshop is a half-day workshop that will open the Extended Semantic Web Conference 2014 and seeks to gather people from all fields to work together to enhance our evaluation methodology. Please see the Call for Papers.

Statistical inference using hypothesis-testing methods is not commonly used to support empirical results reported in the Semantic Web literature. Thus, given the impact that these statistical methods may have on different domains, attendees may see the workshop as a venue to discuss the applicability of these methods in their current studies. Trivial use of hypothesis testing is not in the scope of the workshop. The primary focus, as will be detailed in the call for papers, is on statistical methods to evaluate assertions on performance of complex systems. Hitherto, the focus of the community has been on benchmarking. Currently, it suffers from several problems, e.g., lack of summary statistics, no hypothesis tests, no structured approach to control of experiments, oversimplification of the benchmark in face of complex problems, etc.

Topics to be included (not limited):

Foundations of hypothesis formulation: triangularization, common constructs in the Semantic Web (e.g., performance, completeness) and their validity, typical operationalizations, and quantifiability.
Statistical methods to demonstrate significance and levels of confidence of experimental studies on Semantic Web technologies and empirical results.
Experimental methodologies to design reproducible experimental studies in the context of Semantic Web technologies.
Critical examinations of assumptions inherent in statistical practices in Semantic Web research (e.g., assuming normal distribution of populations).
Limitations of existing statistical methods on empirical studies of Big Data.
Statistical techniques to analyze benchmarks.

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