Measuring the innovation of method knowledge elements in scientific literature

Interest in assessing research impacts is increasing due to its importance for informing actions and funding allocation decisions. The level of innovation (also called “innovation degree” in the following article), one of the most essential factors that affect scientific literature’s impact, has also received increasing attention. However, current studies mainly focus on the overall innovation degree of scientific literature at the macro level, while ignoring the innovation degree of a specific knowledge element (KE), such as the method knowledge element (MKE). A macro level view causes difficulties in identifying which part of the scientific literature contains the innovations. To bridge this gap, a more fine-grained evaluation of academic papers is urgent. The fine-grained evaluation method can ensure the quality of a paper before being published and identify useful knowledge content in a paper for academic users. Different KEs can be used to perform the fine-grained evaluation. However, MKEs are usually considered as one of the most essential knowledge elements among all KEs. Therefore, this study proposes a framework to measure the innovation degree of method knowledge elements (MIDMKE) in scientific literature. In this framework, we first extract the MKEs using a rule-based approach and generate a cloud drop for each MKE using the biterm topic model (BTM). The generated cloud drop is then used to create a method knowledge cloud (MKC) for each MKE. Finally, we calculate the innovation score of a MKE based on the similarity between it and other MKEs of its type. Our empirical study on a China National Knowledge Infrastructure (CNKI) academic literature dataset shows the proposed approach can measure the innovation of MKEs in scientific literature effectively. Our proposed method is useful for both reviewers and funding agencies to assess the quality of academic papers. The dataset, the code for implementation the algorithms, and the complete experiment results will be released at: https://github.com/haihua0913/midmke .