# A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts
## 4 Experiment and Analysis  
This paper's assessments among contributors are derived by modifications of the OSM objects, and the dataset must contain history edit processes, both spatial and attribute aspects, of all objects. Thus, the experimental data in this paper is based on the historical data of OSM in Rutland, England (obtained from [[https://download.geofabrik.de/](https://download.geofabrik.de/)]([https://download.geofabrik.de/](https://download.geofabrik.de/))), as of 12 April 2022. The satellite map data is from Google Maps, as shown in Figure 6a. In this experiment, 724 volunteers participated in the contribution. Due to the processing methods of line objects being similar to the polygon objects, only polygon objects are used for the experiment, and 16,794 different object versions were parsed from the OSM historical data. The dataset contains all the data types such as roads, buildings, etc., and it is rich enough to meet the basic requirements for our study; the version distribution is shown in Figure 6b. According to the model of contributor's reputation evaluation based on semantic similarity of ontology concepts, the semantic similarity is calculated from tag repetition rate and shortest path; the geometric similarity is calculated mainly by shape similarity based on turning function distance, and the topological similarity considers mainly area overlap rate and calculates the reputation of the contributor who participated in the contribution. Through many experiments, the difference in the influence degree of each influencing factor on this model was analyzed, and finally, the parameters in this model were set as \(\mu=5\), \(\omega=0.8\), and \(M=23\). By parsing the historical data of OSM in Rutland, England, a total of 724 users participated in this reputation evaluation model. The users are distributed in the range of 0.5-1 according to their reputation, and they are divided into five intervals in 0.1 increments. The number of people in each interval is 5, 22, 124, 549, and 24, respectively. It can be seen from the number of people in each interval that the distribution of contributors' reputation essentially obeys the normal distribution. As shown in Table 2, the initial reputation of the five users in the range of 0.5-0.6 is 0.75, which means all five users are novice or unskilled contributors. In the range of 0.6-0.7, most of them are novice or unskilled contributors (initial reputation is 0.75). However, a few major contributors (initial reputation is 0.9) begin to appear in this range. In the range of 0.7-0.8, although most users are still novice or unskilled contributors (initial reputation is 0.75), the number of major contributors (initial reputation is 0.9) starts to increase. In the range of 0.8-0.9, most of them are major contributors (initial reputation is 0.9). There are few novice or unskilled contributors (initial reputation is 0.75) and professional contributors (initial reputation is 1). They are all professional contributors within the range of 0.9-1 (initial reputation is 1). It can be seen that on the whole, the contributor's reputation is essentially positively correlated with the initial reputation of users. This means that the higher the initial reputation of contributors, the greater the possibility of their high reputation. Figure 6: Map of Rutland, England: (**a**) satellite map data in Rutland; (**b**) OSM map in Rutland. In order to verify the experimental results, this experiment attached the contributor's reputation to their contributed way objects and displayed way objects in different colors on ArcGIS according to the distribution interval of the contributor's reputation. The effect is shown in Figure 7. Since it is difficult to verify the accuracy of each way object one by one, the sampling analysis method is selected in each reputation interval in this experiment.