## 1 Introduction  
Bridge inspection is critical for bridge evaluation and maintenance decision-making and plays an important role in collecting bridge defect data. During a routine inspection, each element of each bridge component should be inspected. For bridges with many elements, the efficiency of routine inspection is the key problem that should be carefully considered. To improve the efficiency of bridge inspection, many Bridge Management Systems (BMSs) have been developed to assist with the implementation of bridge inspection, such as Pontis BMS [1] and the Chinese Bridge Management System [2]. These systems usually include mobile bridge inspection applications working on smartphones or tablet devices. During a routine inspection, an inspector in the field chooses a particular element from bridge elements' taxonomic lists in a mobile bridge inspection application and records the condition of the element. The inspector will continue the process with the remaining elements. Due to the large number of bridge elements and the limited size of mobile devices, it is difficult for the taxonomic lists in the mobile application to show all the elements. The inspector needs to type a keyword to search for a target element every time he/she performs an element-level inspection. However, the search process can be quite time-consuming. An ontology-based IR method has the potential to overcome the shortcoming by automatically predicting the target element to be inspected. Consequently, the efficiency of bridge inspection can be improved. Recently, the ontology-based IR method has gained extensive attention since this method usually seeks to enhance retrieval accuracy by trying to encode the inspector'ssearching intent and the contextual meaning of the keywords, rather than by exact keywords matching [3]. The ontology-based IR method has been applied in retrieving construction documents [4; 5] and Building Information Model (BIM) resources [6; 7; 8; 9]. [PERSON] et al. [10] validated that the concrete bridge rehabilitation project management ontology (CBRPMO) can improve the search for project information compared with manual searching. [PERSON] et al. [11] demonstrated that the ontology-based IR method could make it more convenient for sensory data retrieval. However, in the field of bridge engineering, ontology applications are still at the conceptual phase stage [12]. Existing ontologies mainly focus on bridge maintenance [12] and rehabilitation [10]. These bridge ontologies do not fully cover bridge inspection information, and the construction of these ontologies requires a lot of manual work [10; 13]. This study aims to propose an enhanced information retrieval method based on ontology to improve the efficiency of bridge inspection. To achieve this purpose, an improved seven-step method based on automatic mapping technology is proposed to accelerate the construction of a new ontology, bridge inspection ontology (Blontology). A new software architecture for integrating ontology is proposed and a prototype system based on the proposed architecture is developed. With these contributions, the ontology-based IR method is established. A prediction algorithm based on the proposed IR method can correctly predict the target element to be inspected, which can avoid the process of inputting a keyword to search for the target element. The remaining contents of this study are organized as follows: The related works are thoroughly summarized in Section 2. Section 3 introduces an overall process of the proposed ontology-based IR method for bridge inspection. In Section 4, an improved seven-step method for quickly building the Blontology is introduced. In Section 5, a bridge inspection prototype system is developed to realize the proposed IR method by SPARQL query (an ontology-based data access [14]). A proposed algorithm for predicting the target element and a software architecture for integrating ontology are also described in this section.