# Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles
## 2 Record Linkage Algorithm
### Generation of Features for Comparison
#### 2.1.3 Area of Interests as ASJC Codes, Features 7 and 8  
The ASJC codes describe the subject areas of journals and, as a consequence, subject areas of papers published in those journals. Based on that, research areas of authors of papers can be determined, taking the most common ASJC codes assigned to his/her publications.  
ASJC codes were not available for patents, and we deduced them from patent abstracts. For this task, we leveraged multi-label classification models--supervised learning models dedicated to problems where there are one or more labels per sample. We use multi-label classification models. Every journal article becomes a training sample; this means a vector with text features extracted from papers labeled with multiple ASJC codes. We apply models trained on text features extracted from papers to text features extracted from patents to determine ASJC for patents. The algorithm is depicted in Figure 3.  
Figure 2: Generation of numerical representations of titles and abstracts.  
We assumed that abstracts of both kind of documents were similar, and that sets of DBpedia resources extracted from both types of documents were overlapping. Indeed, 86.4% of DBpedia resources extracted from patents were also extracted from papers.  
We implemented three multi-label classification algorithms: Binary relevance (BR), label powerset (LP), and multi-label back-propagation (BP-MLL).  
BR is a method where one binary classifier is trained for each label. In BR, labels are treated independently, which is the main drawback of this method. Given an unseen instance, BR predicts a set of labels being a union of all positive results of base classifiers. An important advantage of BR is its ability to generalize beyond available label combinations. It means that BR can predict a combination of labels that were not present in training datasets. Any binary classifier might be used as a base model in BR, here a decision tree classifier (DT) was used.  
LP in turn, converts each unique label combination into a separate class and applies a single multi-class classifier. The advantage of this approach is that it estimates label dependencies with only one classifier. A profound drawback of LP is that it can predict only the label combinations present in the training set. It is also prone to underfitting when the label space is large. Here, we use Gaussian naive Bayes (GNB), but any multi-class classifier can be used with LP.  
BP-MLL [34] is a modification of a classical back-propagation algorithm used in the training of neural network. BP-MLL uses a novel error function capturing the characteristics of multi-label learning. It gives higher ranks to labels that belong to an instance than to those not belonging to that instance. Threshold is applied to convert achieved continuous model outputs into discrete predictions.  
A set of ASJC codes predicted for a patent is first compared with a single ASJC code assigned to a scholar in the database in one of the columns of the table with authors, then it is compared with sets of ASJC codes assigned to papers authored by the scholar. As a result, we calculate two scores related to ASJC codes. The first score is just a binary indicator, whether or not an ASJC code assigned to a famous author belongs to the set of ASJC codes predicted for the patent. The second score is the maximum Jaccard similarity between ASJC codes for a patent and each set of codes assigned to papers of a scholar.