# Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles
## 4 Conclusions  
The evaluation results showed that fuzzy name matching, comparison of abstracts of patents and papers and comparison of ASJC codes improved matching quality when compared to the baseline solution that links records only by name attributes. Experiments conducted on different feature subsets indicate that the performance of the threshold-based classifier strongly depends on the feature subsets used. On the other hand, classification methods based on machine learning perform best on complete feature sets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**FS**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**\(\mathbf{F_{1}}\)**} & **AUC** & **AP** \\ \cline{3-10}  & & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Train** \\  
Our classification system uses ASJC codes at the article level, but the ASJC codes are assigned at the journal level. This simplification may lead to some bias, especially for non-mainstream articles and multi-disciplinary works. Usage of title, keywords and abstract only partially solve this problem. The multi-label classification helps to reduce false negatives and helps here. We checked dozens of articles and did not find errors by assuming that journal ASJC codes accurately depict each article. Moreover, multi-label classification can properly handle multi-disciplinary and interdisciplinary researchers. Our algorithm assigns a set of scientific domains to every researcher. The domains, ASJC codes, could belong to similar or different scientific areas. The risk of improper results for scientists with multi-/interdisciplinary studies exists when such a scientist is the author of papers/patents from different disciplines A and B and she/he has no paper/patent classified for both A and B. In such a case, the algorithm returns two scientists with the same name: the first scientist publishes in discipline A and the second scientist publishes in discipline B. If databases have an article or a patent for this scientist in both disciplines, this article/patent links two clusters creating one. We manually checked our databases and found no such cases. ASJC codes were not available for patents, but patents, in turn, are categorized using codes from the International Patent Classification (IPC) system. ASJC and IPC systems are entirely disjointed, and mapping between them does not exist. We examined both sets of codes and we attempted to map them, first manually, then using machine learning techniques. Neither method gave satisfactory results. The presented solution automates the process of record linkage in the Shanghai Talents Center. Integration of the patents database and scientific articles database improves systems' abilities to search for experts and gives a better description of scientists and helps to detect cases where authors first publish a substantial part of their invention as an article in a scientific journal and then want to patent it. In a production environment, our classifier may produce both false positives and false negatives. To mitigate the former, we proposed to collect record pairs marked by the algorithm as true matches and present them to domain experts who will make a final decision. Due to the presence of a large number of record pairs, we determined another threshold. Results produced by the final classifier with confidence between the lower and upper threshold are presented to annotators and results with confidence above the upper threshold are accepted without manual approval. Fighting with a high number of false negatives is more complicated. Moreover, our proposal is lowering values of the threshold applied to classifier outcomes; however, it leads to a higher number of record pairs that are presented to domain experts. Further work includes implementation of the proposed solution in production systems and evaluation on similar datasets, for example on the Polish Patents Database. Conceptualization, R.N. and W.F.