Design Rationale Capture as Knowledge Acquisition Tradeoffs in the Design of Interactive Tools

Proceedings of the Machine Learning Workshop. 1991
Design Rationale Capture as Knowledge Acquisition Tradeoffs in the Design of Interactive Tools
Thomas Gruber, Catherine Baudin, John Boose, Jay Weber

This paper introduces a panel to be held at the Knowledge Acquisition Track of the Machine Learning Workshop (ML91). This panel will focus on the problem of acquiring design rationale knowledge from humans for later reuse.

The design of tools for design rationale capture reveals several fundamental issues for knowledge acquisition, such as the relationships among formality and expressiveness of representations, and kinds of automated support for elicitation and analysis of knowledge.

This paper sets the background for discussion by identifying dimensions of a design space for design rationale tools, and then includes position statements from each panelist arguing for various positions in this space.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.