## 1 Introduction  
With the continuous progress of computers and information technology, the construction industry is gradually moving towards digitization and informatization. In this context, BIM technology has been widely used in the construction field, with many building projects adopting BIM for design, construction, and operation management [1]. BIM technology covers the entire lifecycle of a building, and in the process of constructing transportation infrastructure, information from BIM models can be utilized in various stages of construction [2, 3]. Interacting and integrating information from BIM models into different systems can improve the efficiency and accuracy of information transmission in construction projects. However, the construction of transportation infrastructure involves diverse projects of different types, and different stages of the same project have varying requirements, resulting in the heterogeneity of indicators. In theory, the information contained within a BIM model could encompass the entire lifecycle of a building. However, due to the aforementioned phenomenon of indicator heterogeneity, software engineers, when tasked with constructing a BIM information sharing system, encounter concepts that are semantically related yet have different names across various indicators. The semantic associations among these concepts lack a quantifiable metric, leading engineers to rely on subjective judgment to assess their semantic associations. Such a workflow not only proves to be inefficient but also introduces numerous errors, significantly impeding the integration and sharing of BIM information. Semantic similarity is a metric used in the fields of natural language processing (NLP) and knowledge representation to measure the degree of semantic similarity between two texts or concepts. It is a method for comparing and quantifying semantic associations between texts or concepts [4]. There are various methods to calculate semantic similarity, among which those combined with ontology technology are known as ontology-based semantic similarity. Ontology-based semantic similarity is a metric used to measure the degree of semantic similarity between concepts or instances in an ontology [5; 6]. When calculating ontology-based semantic similarity, structural information and property information from ontology trees can be utilized to compute the semantic relevance of concepts in a domain, addressing the problem of semantic inconsistency in data interaction involving multiple heterogeneous sources [7; 8]. To ensure the reliability of ontology-based semantic similarity calculation results, it is necessary to construct a complete domain ontology model and consider multiple dimensions of semantic information comprehensively. Currently, ontology semantic similarity can be calculated from various dimensions, such as path distance, hierarchical structure, attribute features, and semantic relationships [9], but the accuracy of the calculation depends on the fit between the ontology model and the domain. Ontology technology has been widely applied in the construction field, including the construction and maintenance of building knowledge bases, semantic search and inference of building information, and knowledge management of BIM models [10; 11; 12; 13]. In the construction field, the use of existing ontology-based semantic similarity calculation methods often yields favorable results. However, applying the same methods in the domain of transportation infrastructure proves to be less satisfactory. Compared to traditional buildings, transportation infrastructure has more complex structures, larger spans, and involves more elements, which means that after ontology mapping, transportation infrastructure ontology models have more nodes, more hierarchical levels, and stronger attribute diversity. These characteristics indicate that using existing ontology-based similarity calculation methods to compute the semantic similarity in the field of transportation infrastructure may affect the precision of results.