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The knowledge retrieval evaluation mechanism allows system developers to maintain the knowledge retrieval system with ease and meanwhile enhance the accuracy.
Specifically, this study involves the following tasks: (i) proposes a general knowledge retrieval framework based on the analysis result of knowledge retrieval, (ii) designs the knowledge retrieval evaluation framework using Six Sigma's Define-Measure-Analyze-Improve-Control (DMAIC) process and (iii) develops the related technologies to implement the knowledge retrieval evaluation mechanism.
Nowadays, there is not a standard evaluation framework for knowledge retrieval evaluation, because the evaluation set up is still technology-dependent, focusing on specific elements of the search context.
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In order to improve the performance of knowledge retrieval, this paper proposes an evaluation mechanism using Six Sigma methodology to help developers continuously control the knowledge retrieval process.
The laboratory-based evaluation is not suitable to evaluate the knowledge retrieval process, since knowledge is dynamic, constantly changing and evolving.
In fact, one of the challenges of knowledge management in cloud computing, and more generally information search, retrieval, evaluation and organization lies in the degree of complexity and sophistication of search engines [11], since in cloud contexts those are not only expected to retrieve the information, but also to advise its relevance and trustworthiness.
Retrieval evaluation with incomplete information.
Reliable Information Retrieval Evaluation with Incomplete and Biased Judgements.
The concepts include knowledge acquisition, knowledge engineering, knowledge management, knowledge level, knowledge retrieval, knowledge modeling, knowledge protection, knowledge retention, knowledge deletion and knowledge privacy.
MedDRA® is used for data entry, retrieval, evaluation, and presentation.
Knowledge retrieval is a decisive part of the performance of a knowledge management system.
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