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| Discipline Identification Methods for Knowledge Units: A Comparative Study towards Feature Mining |
| Cao Yujie1, Xiang Rongrong1, Mao Jin2, Wang Shiyun2 |
1.School of Information Management, Central China Normal University, Wuhan 430079
2.School of Information Management, Wuhan University, Wuhan 430072 |
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Abstract The features of knowledge units are the basis of the discipline identification of knowledge units. Mining the key features of knowledge units can help improve performance, so as to better serve the study of interdisciplinary research at the knowledge content level. In this study, with the help of 16 methods of discipline identification for knowledge units, we compared and analyzed the discriminative performance of these methods for knowledge units with different word frequencies and disciplinary coverages. Further, we evaluated the effect of the three features and feature combinations of disciplinary importance, disciplinary relevance, and disciplinary discriminability implied by the methods to mine the subset of features with the best effect. We also constructed a test dataset based on data from the cross-cutting field of “computational medicine.” The experimental analysis results showed that the combined use of the three features achieved better performance on all groups, while the performance advantage of disciplinary importance indicates that it is the most important among the three features; the discipline identification of high-frequency words needs to focus on disciplinary importance, while low-frequency words need to focus on disciplinary importance. For knowledge units with multidisciplinary coverage, it is necessary to consider disciplinary differentiation in addition to disciplinary importance. The findings of this study provide theoretical guidance and practical suggestions for the optimization of discipline identification methods for knowledge units.
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Received: 01 November 2022
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