# A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts
## 3 Methods
### Contributor's Evaluation Reputation
#### 3.3.2 Semantic Similarity of Object Version  
Semantics describe mainly nongeometric characteristics of the features, determined by semantic similarity between different versions. In this paper, each entity is classified according to the classification rules, and the classified object concepts form a classification tree structure, as shown in Figure 3. The hierarchical classification is simple, so this paper chooses the edge-based similarity measure: the semantic similarity between two concepts is obtained by calculating the links or depths between concepts. Concept attributes and concept tree structure are the main factors that affect the semantic similarity of ontology  
Figure 3: Part of volunteered geographic information ontology.  
-based concept. In this paper, the similarity of concept attribute and semantic distance of concept tree structure are used as indicators to calculate the semantic similarity between concepts.  
(1) Similarity of concept attribute.  
Entities themselves are described by conceptual attributes. Different concepts can be distinguished to a certain extent according to the differences between their attributes. It is generally believed that the more the same attributes exist between concepts, the higher the similarity between concepts. In this paper, repetition rate is only one aspect of the semantic similarities between two concepts. The literature [30,47] proposes that each OSM element has some tags, and each tag is represented by a form of key-value pair to describe its attributes which combined with the specific expression of OSM, as shown in Table 1. Because the conceptual attributes are explained by tags, it is appropriate to use tag repetition rate as one of the metrics to calculate the semantic similarity between two concepts. The similarity of concept attribute between versions \(x\) and \(y\) is expressed by the tag repetition rate, and its specific calculation formula is as follows:  
\[Sim_{tag}=\frac{tag_{x}\cap tag_{y}}{tag_{x}\cup tag_{y}} \tag{1}\]  
(2) Semantic distance of concept tree structure.  
Semantic distance is another important indicator to measure the semantic similarity of ontology concept. The smaller the semantic distance between concepts, the higher the similarity between concepts. Therefore, this paper uses the edge-based similarity measurement method and uses the shortest path length between the features of the ontology structure graph as the semantic distance indicator, and the semantic distance is defined as the sum of the shortest path lengths of two concepts from any common ancestor node \(LCA(X,Y)\) to \(x,y\), respectively [48]:  
\[distance=length(LCA(X,Y),X)+length(LCA(X,Y),Y) \tag{2}\]  
To sum up, the similarity of concept tree structure can be obtained, and the specific calculation is as follows [48]:  
\[Sim_{structure}=\frac{\mu}{distance+\mu} \tag{3}\]  
where \(\mu\) is determined according to the maximum value of the shortest path length between two concepts in the concept tree structure. Combining the similarity of concept attribute and the similarity of concept tree structure, the semantic similarity based on ontology concept is calculated by weighted summation [48]:  
\[Sim_{semantic}=\omega\cdot Sim_{tag}+(1-\omega)Sim_{structure} \tag{4}\]  
where \(\omega\) is the weight to calculate the semantic similarity of ontology-based concepts, which is a real number distributed between (0,1).