## 2 Related Work  
The ontology model can store rich semantic, structural, and attribute information, and it allows similarity calculations from multiple perspectives. Early research on ontology similarity mainly focused on analyzing similarity from a single perspective. [PERSON] et al. [5] categorized these approaches into Edge-Counting, Feature-based, and Information-Content similarity calculation methods. The method of measuring similarity by the length of the path between two nodes in the ontology is known as the path Edge-Counting method. [PERSON] and [PERSON] [14] proposed a model using edge annotation to measure semantic similarity in WordNet classifications. They annotated directed edges in the ontology model based on the types of relationships between concepts. Different types of paths were assigned different weights in the calculation to align the results more closely with human judgment. However, due to the involvement of seven free parameters in the algorithm, the stability of the calculation results is not sufficient. The Feature-based method determines the similarity between two concepts by analyzing their common and different properties. [PERSON] and [PERSON] [15] proposed a similarity calculation process that subdivides properties into property names, property data types, and property instance data, and uses string similarity calculation methods to calculate the similarity of property names and property data types with a comprehensive weighting. However, the accuracy of the results obtained by this method is largely influenced by manually setting the weights, making it subjective. Moreover, this method lacks universality for different ontologies. The Information-Content method combines information entropy with ontology relationships. [PERSON] [16] proposed a model called DiShin, which can compute the shared information content between two ontology concepts based on the information content of their disjunctive common ancestors. This method introduces information entropy and analyzes the information content of two concepts, but it does not utilize higher-level ontology features in the model. The aforementioned similarity calculation methods utilize information from the ontology to measure the relevance between concepts but only use partial information from the ontology model. In recent years, research on ontology-based semantic similarity no longer focuses solely on individual similarity measures but instead combines multiple measures to perform mixed calculations, involving more comprehensive elements and obtaining more reliable results. [PERSON] et al. [17] proposed a contributor's reputation model based on the semantic similarity of ontology concepts. In this method, Edge-Counting similarity and Feature-based similarity are used as indicators to calculate the semantic similarity between concepts. Then, the evaluation reputation of Volunteered Geographic Information (VGI) contributors is computed based on the integrated semantic similarity, geometric similarity, and topological similarity of the object versions. As semantic similarity is just one aspect of calculating the contributor reputation, this method analyzes semantic similarity from the perspectives of edges and properties in the ontology. [PERSON] et al. [18]proposed an improved semantic similarity algorithm that weights the Edge-Counting similarity and Information-Content similarity to calculate the comprehensive semantic similarity between concepts in WordNet. The Edge-Counting similarity comprehensively analyzes various structural features of the ontology model, including the deepness, density, and importance of edges. However, as the aim of this method is to calculate the semantic similarity between WordNet concepts, it cannot use the similarity of properties between concepts as an indicator. [PERSON] et al. [19] improved the classic similarity calculation model based on semantic distance, information content, and concept property, and also proposed a similarity calculation model based on the view of concept's sub-nodes coincidence.