Dedal: Using Domain Concepts to Index Engineering Design Information

COGSCI 92, Proceedings of the 14th conference of the Cognitive Science Society. 1992
Dedal: Using Domain Concepts to Index Engineering Design Information
Catherine Baudin, Jody Gevins, V.Baya , Ade Mabogunje

The goal of Dedal is to facilitate the reuse of engineering design experience by providing an intelligent guide for browsing multimedia design documents. Based on protocol analysis of design activities, we defined a language to describe the content and the form of technical documents for mechanical design.

We use this language to index pages of an Electronic Design Notebook which contains text and graphics material, meeting reports and transcripts of conversations among designers. Index and query language with concepts from a model of the designed artifact.

The information retrieval mechanism uses heuristic knowledge from artifact model to help engineers formulate questions, guide the search for relevant information and refine the existing set of indices. Dedal is a compromise between domain-independent argumentation-based systems and pure model-based systems which assume a complete formalization of all design documents.

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.