# Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles
## 3 Experiments and Discussion
### Evaluation of Record Linkage
#### 3.2.1 Evaluation of the Matching Complexity  
We evaluated matching complexity using the following metrics: reduction ratio, pair completeness and pair quality. The reduction ratio (RR) provides information about how many candidate record pairs were generated by an indexing technique, compared to all possible record pairs. Pair completeness (PC) is the number of true matching record pairs that were generated, divided by the total number of true matching pairs in the full comparison space. Pair quality (PQ) is the number of candidate record pairs that correspond to true matches that were generated by an indexing technique, divided by the total number of candidate record pairs that were generated. It corresponds to precision.  
In this study two indexing techniques in five different settings were compared:  
1. B-EN (raw): blocking applied to raw English names;
2. B-EN: blocking applied to normalized English names;
3. B-CN: blocking applied to raw Chinese names;
4. B-CN + SN-EN: combination of B-CN and sorted neighborhood (SN) applied to normalized English names;
5. B-EN + SN-CN: combination of B-EN and sorted neighborhood applied to raw Chinese names.  
The last two methods are hybrid techniques that produce a union of the candidate pairs created by each component indexing method. Additionally, the presented techniques were compared to a baseline solution, where no indexing is applied and candidates are created by taking a Cartesian product of the two datasets.  
Statistics for each indexing technique calculated on a training subset are presented in Table 3. The fewest candidates were created by B-EN (raw), but PC for this method was only less than 30%. Normalization of English names improved statistics of B-EN three times when compared to B-EN (raw). B-CN outperformed B-EN in terms of PC and PQ and hybrid methods outperformed simpler techniques. A comparison of the hybrid methods on different window sizes is depicted in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indexing Method** & **True Links** & **Candidates** & **RR** & **PC** & **PQ** \\ \hline Cartesian product & 13,769 & 216,761,108 & - & - & - \\ \hline B-EN (raw) & 4084 & **6912** & **1.000** & 0.297 & 0.591 \\ \hline B-EN & 12,607 & 45,621 & 1.000 & 0.916 & 0.276 \\ \hline B-CN & 12,744 & 16,919 & 1.000 & 0.926 & **0.753** \\ \hline B-CN + SN-EN & 13,695 & 114,523 & 0.999 & 0.995 & 0.120 \\ \hline B-EN + SN-CN & **13,704** & 65,327 & 1.000 & **0.995** & 0.210 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics for each tested variant of indexing measured on a training subset of the reduced dataset. The best values of each statistic are in bold. For the last two methods their best variants (window = 3) are reported.  
B-EN + SN-CN applied to the reduced dataset outperformed B-CN + SN-EN for all window sizes. A window size equal to 3 was selected as the best, because the next window (5) provided a relatively small improvement in terms of PC, but a significant increase in the number of candidates. Statistics for the selected indexing configuration measured on the testing subset are depicted in Table 4.  
Figure 4: Comparison of two variants of hybrid indexing.