## 2 Related Work
They introduced a BP neural network in the weighted calculation and optimized the neural network using Simulated Annealing (SA) to obtain the weights of various similarities, avoiding the subjectivity of manually setting weights and enhancing the accuracy of integrating ontology similarities. However, this method is specifically designed for command information concepts and is challenging to directly apply to the domain of transportation infrastructure. [PERSON] et al. [20] combined ontology technology to propose a geographic information semantic similarity calculation method. In the geographic information domain, spatial distribution characteristics and geometric features are essential for analysis. Hence, this method focuses more on the types and ranges of properties used in the model. It evaluates the contribution of ontology properties, measures the influence of relative positions in the ontology hierarchy structure, and calculates the geometric similarity of geographic spatial entities to obtain geographic semantic similarity more accurately and effectively. For the transportation infrastructure domain, spatial distribution characteristics and geometric features are equally important for analysis. However, the current similarity calculation methods for transportation infrastructure do not fully utilize the relevant attributes in the ontology. In general, hybrid similarity calculation methods often yield superior results compared to approaches focused solely on individual factors. Hybrid semantic similarity is derived by performing additive weighting on different similarity outcomes, requiring the computational model to express relationships among these diverse similarities. Nevertheless, existing hybrid similarity calculation models often demand extensive data during training, and the determination of model weights heavily relies on expert opinions and empirical data, introducing subjectivity into weight allocation. Neural networks, with their potent nonlinear modeling and feature learning capabilities, can objectively and accurately represent relationships between similarities after training. Therefore, we opt for neural networks to perform hybrid similarity calculation. The integrated computation employed in this study involves additive weighting of Edge-Counting, Feature-based, and Information-Content similarities. During training, the three individual factor similarities serve as inputs to the neural network, while the comprehensive semantic similarity of the training set serves as the output. Both the BP neural network and most of the currently commonly used neural networks have shown admirably performance when handling the aforementioned weighted task. Furthermore, given the mature state of research concerning BP neural networks, training and parameter tuning for BP neural networks are relatively straightforward. Hence, we have chosen to employ a BP neural network for the task of weights allocation in this paper. Nonetheless, it is crucial to acknowledge that BP neural networks come with inherent limitations that can impact results. To mitigate these limitations, we employ the SHO algorithm to optimize the BP neural network, aiming for enhancing the accuracy computational outcomes.