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| Comparisons of Data Set Construction Methods in Domain Analysis of Science and Technology: Consistency and Reliability |
| Chen Guo1,2, Shao Yu1, Wang Yuefen1,2 |
1.Department of Information Management, Nanjing University of Science and Technology, Nanjing 210094
2.Jiangsu Science and Technology Collaborative Innovation Center of Social Public Safety, Nanjing 210094 |
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Abstract In scientific and technical intelligence analysis, there are several methods in which documents can be collected; however, differences between these methods and their reliabilities are not well-understood. A manner in which scientific intelligence analysis can be promoted is to explore methods to collect field documents that can guarantee the reliability of analysis results. Therefore, this study aims to address this question quantitatively via an empirical analysis. First, the field of artificial intelligence is considered as an example along with the various task scenarios of scientific and technical intelligence analysis (including different analysis elements, element numbers, and sorting methods). Three progressive experiments are then designed in this study, each addressing one of the following: (1) the differences among the mainstream methods used to collect documents; (2) the reliability of these methods; and (3) the reliability of the final analysis results when the different methods are combined. The experimental results demonstrate that there is a slight difference and high reliability between these collection methods in intelligence analysis tasks of coarse-grained elements such as countries. However, when the intelligence analysis tasks can implemented by different researchers, the results of the existing collected methods are evidently different and their reliability is low. In different intelligence analysis tasks, we can select a relatively suitable manner to collect documents. The improvement in the reliability is not evident when the documents are collected by combining different methods. In other words, optimizing the collection of documents is crucial for the analysis of scientific and technical intelligence. Finally, based on quantitative indicators, this paper presents method to improve the reliability of scientific and technical intelligence analysis in terms of collecting documents along with future scope.
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Received: 15 October 2019
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