# A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts
###### Abstract  
Due to their status as non-professionals, the reputations of Volunteered Geographic Information (VGI) contributors have a very important impact on data quality. In the process of contributor reputation evaluation in OpenStreetMap (OSM), it is very difficult to calculate the semantic similarity between object versions contributed by volunteers. Aimed at this issue, this paper proposes a model of contributor's reputation based on semantic similarity of ontology concepts. Firstly, contributors are classified into three categories based on an improved WPCA and classification method. Then, an initial reputation is set for every OSM user in each class according to these categories and related research. Secondly, the related concept ontology is constructed for OSM entities; then, the semantic similarity of the object version is calculated according to the similarity of concept attributes and the semantic distance of concept. The contributor's evaluation reputation is computed by synthesizing the semantic similarity, geometric similarity, and topological similarity of object versions. Thirdly, the contributor's evaluation reputation and the initial reputation is aggregated to obtain the contributor's reputation; finally, the OSM data of Rutland, England, is used as an example to verify the validity of our model. The experimental results show that the proposed model can obtain a more comprehensive contributor's evaluation by fusing with the semantic similarity of ontology concept. The evaluation bias caused only by the semantic change between versions can be eliminated. Moreover, the obtained user's reputation is positively correlated with the data quality. The contributor's reputation evaluation method proposed in this paper is an effective method for evaluating the contributor's reputation in OSM-like systems. contributor's reputation; semantic similarity; ontology; VGI; OSM 2022  
crowdsourcing geographic data from the perspectives of the overall evaluation and individual evaluation [6]. The overall evaluation mainly compares the data with the authoritative data [7; 8], but this method has some limitations, such as the authoritative data being difficult to obtain and the slow update. In order to solve the former limitations, many scholars began to explore the evaluation methods for the individual quality of crowdsourcing objects. This type of approach evaluates data quality by analyzing the evolution of the dataset itself rather than comparing it with external datasets. In view of the importance of contributors in VGI, many methods were proposed to evaluate their reputation base on either direct or indirect scoring method in which a direct-scoring-based evaluation method requires contributors to explicitly score the object. For example, [PERSON] et al. proposed an explicit evaluation mechanism for crowdsourcing geographic information, stressing the notion of people-object transitivity: the degree of reliability associated with a person can be reflected in a related entity [9]. This method needs to obtain information such as social relations among contributors, which increases extra workload. Therefore, as an important method for quality assessment, it concerned many scholars. [PERSON] et al. believed that data credibility can be used as a measurement index of contributor's reputation--contributor's reputation is the average value of their contribution data credibility--and proposed a model to measure data credibility and contributor's reputation by analyzing geometric and topological attributes [10]. [PERSON] et al. believed that the credibility of data is related to the reputation of current contributors and previous contributors, and they proposed a calculation model based on OSM user's reputation to evaluate volunteered geographic information from the perspective of credibility [11]. To sum up, many scholars have proposed that the semantic similarity of objects should be considered in the process of user reputation calculation [10; 11].