# A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts
## 4 Experiment and Analysis
Firstly, five contributors are selected in each reputation interval with the interval of 0.1 to analyze their contributions, and a total of 25 contributors are sampled to participate in the evaluation, including 3417 valid versions. Finally, according to the sampling situation, the contribution data effect can be divided into three categories: error (single evaluation value less than 0.5), poor (single evaluation value between 0.5 and 0.75), and good (single evaluation value greater than 0.75). Among the sampled data, there are 17 error data cases, 337 poor data cases, and 3063 good data cases. According to the sampled data results, as shown in Table 3, the OSM data quality in this region is generally good, with only a few error data cases. Among the data provided by contributors with low reputation, the proportion of error data and poor data is high. For example, when the reputation of contributors is lower than 0.8, there are 16 error data cases, accounting for approximately 94.1% of the error data. With the improvement of contributors' reputation, the proportion of data with higher contribution quality gradually increases, and the proportion of error data shows a downward trend. For example, when the contributor's reputation is higher than 0.8, there are 2948 contributed data objects, accounting for 96.2% of the good data. The contributor's reputation is within the reputation interval of 0.9-1, and there is no error data. It can be seen that the quality of data increases with the reputation of contributors, and there is a positive correlation between data quality and the contributor's reputation. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Initial Reputation** & **0.5–0.6** & **0.6–0.7** & **0.7–0.8** & **0.8–0.9** & **0.9–1** & **Total/Person** \\ \hline
0.75 (Novice or unskilled contributors) & 5 & 21 & 116 & 19 & 0 & 161 \\
0.9 (Major contributors) & 0 & 1 & 7 & 523 & 0 & 531 \\
1 (Professional contributors) & 0 & 0 & 1 & 7 & 24 & 32 \\ Total/person & 5 & 22 & 124 & 549 & 24 & 724 \\ \hline \end{tabular}
\end{table} 
Table 2: Sample table of the relationship between contributor reputation and initial reputation. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Effect** & **Interval** & **0.5–0.6** & **0.6–0.7** & **0.7–0.8** & **0.8–0.9** & **0.9–1** & **Total/Version** \\ \hline Error & 10 & 5 & 1 & 1 & 0 & 17 \\ Poor & 3 & 10 & 78 & 175 & 71 & 337 \\ Good & 9 & 14 & 92 & 580 & 2368 & 3063 \\ Total/version & 22 & 29 & 171 & 756 & 2439 & 3417 \\ \hline \end{tabular}
\end{table} 
Table 3: Sample table of the relationship between contributor reputation and data quality. This paper selects three examples to further verify the effectiveness of the model proposed in this paper. The above Figure 8a shows the object with the object id of 4406271, contributed by the user (id: 280348). It can be clearly seen from the image that the dataset is error data, and the object is below the reference real dataset, which does not match the real dataset in geometry and topology. Figure 8b shows the object with the object id of 43407756 contributed by the user (id: 12355). It can be seen that this data object has a high degree of matching with the real dataset in terms of geometry and topology similarity, which is more in line with the real situation on the ground and has high data quality. Figure 8c shows that the object with the object id of 3700148 contributed by the user (id: 1377) has a high degree of matching with the actual object in geometry and topology. The object tag provided by the contributor is _amenity_ = _school_, which has semantic deviations. Therefore, combining semantic similarity to evaluate contributor's reputation can improve the accuracy of the evaluation. The scores of specific evaluation indicators and the contributor's reputation of semantic similarity, geometric similarity, and topological similarity are shown in Tables 4 and 5, respectively.