Evaluating epistemic uncertainty under incomplete assessments

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The thesis of this study is to propose an extended methodology for laboratory based Information Retrieval evaluation under incomplete relevance assessments. This new methodology aims to identify potential uncertainty during system comparison that may result from incompleteness. The adoption of this methodology is advantageous, because the detection of epistemic uncertainty - the amount of knowledge (or ignorance) we have about the estimate of a system's performance - during the evaluation process can guide and direct researchers when evaluating new systems over existing and future test collections. Across a series of experiments we demonstrate how this methodology can lead towards a finer grained analysis of systems. In particular, we show through experimentation how the current practice in Information Retrieval evaluation of using a measurement depth larger than the pooling depth increases uncertainty during system comparison.
Original languageEnglish
Pages (from-to)811-837
Number of pages27
JournalInformation Processing and Management
Issue number2
Publication statusPublished - Mar 2008


  • information retrieval evaluation
  • incompleteness
  • system comparison
  • test collections

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