Development of Industrial Knowledge Management Applications with Case-Based Reasoning

Studies in Computational Intelligence: Successful Case-Based Reasoning Applications – 1, Volume 305/2 010. P 53-82. Springer
Development of Industrial Knowledge Management Applications with Case-Based Reasoning
Mehmet H.Goker, Catherine Baudin, Michel Manago, Mehmet H.Goker, Catherine Baudin, Michel Manago

The successful development, deployment and utilization of Case-Based Reasoning Systems in commercial environments require the development team to focus on aspects that go beyond the core CBR engine itself. Characteristics of the Users, the Organization and the Domain have considerable impact on the design decisions during implementation and on the success of the project after deployment.

If the system is not technically and organizationally integrated with the operating environment, it will eventually fail. In this chapter, we describe our experiences and the steps we found useful while implementing CBR applications for commercial use. We learned these lessons the hard way. Our goal is to document our experience and help practitioners develop their own approach and avoid making the same mistakes.

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.