# Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles
###### Abstract  
We present an algorithm to find corresponding authors of patents and scientific articles. The authors are given as records in Scopus and the Chinese Patents Database. This issue is known as the record linkage problem, defined as finding and linking individual records from separate databases that refer to the same real-world entity. The presented solution is based on a record linkage framework combined with text feature extraction and machine learning techniques. The main challenges were low data quality, lack of common record identifiers, and a limited number of other attributes shared by both data sources. Matching based solely on an exact comparison of authors' names does not solve the records linking problem because many Chinese authors share the same full name. Moreover, the English spelling of Chinese names is not standardized in the analyzed data. Three ideas on how to extend attribute sets and improve record linkage quality were proposed: (1) fuzzy matching of names, (2) comparison of abstracts of patents and articles, (3) comparison of scientists' main research areas calculated using all metadata available. The presented solution was evaluated in terms of matching quality and complexity on \(\approx\)250,000 record pairs linked by human experts. The results of numerical experiments show that the proposed strategies increase the quality of record linkage compared to typical solutions. probabilistic record linkage; fuzzy string matching; text features extraction; supervised learning; DBpedia; All Science Journal Classification (ASJC) 2021202020275 Septembertifiers of two records being compared. In [2] the authors analyzed the performance of several identifiers used in deterministic record linkage. The performance of deterministic algorithms on different datasets was validated in [3,4]. The comparison of deterministic and public domain software applications was conducted in [5]. Probabilistic record linkage methods are mainly based on the Fellegi-Sunter framework [6]. Extensions include adding approximate string matching [7] or methods to reduce problem complexity [8,9]. More recent probabilistic approaches depict the record linkage problem as a binary classification problem or a clustering problem. It has been recognized [10] that the algorithm given by [PERSON] and [PERSON] is equivalent to the Naive Bayes classifier. Other classification techniques have also been evaluated, including single-layer perceptrons [11], decision trees [12] and Support Vector Machines [13]. Record linkage as clustering was evaluated [14], using either iterative or hierarchical clustering [15,16] or graph-based techniques [17,18]. Such unsupervised learning methods are reported to give high quality linkage results, but are often impractical when used with large datasets due to their high computational requirements. The problem of record linkage is applied mostly in the health sector [19, 20, 21], but also in national censuses [22], national security [23], bibliographic databases [24, 25, 26] and online shopping [27]. The presented algorithm links patent and scholar records, such that the scholar is the same person as one of the patent's inventors, as depicted in Figure 1.  
The records cannot be linked using simple SQL commands because patent inventors are identified only by their names and there are no other attributes available, such addresses, birth dates, residential areas, or names and addresses of organizations. Linking records using only names is not straightforward because the way the names are stored in both databases varies. Moreover, the majority of records describe authors of Chinese origin with short and simple names. Thus, multiple authors share the same name [28]. Affiliations could not be used because the patent database does not store them. As a baseline, we consider a simplified record linkage pipeline representing a linkage procedure performed by a human annotator without any additional knowledge about the records being linked.