## 2 Literature Review
### Ontology Application in the Construction Industry  
Ontology can be regarded as an explicit specification of a representational vocabulary for a shared domain of discourse, which usually includes classes, relations among these classes, instances, and other objects [23]. With these merits, ontology can be used to improve IR performance. Early applications focused on searching some technical documents. [PERSON] [4] enhanced the construction documents search service in the e-COGNOS system using ontology. [PERSON] et al. [5] used a domain ontology to assist with the partitioning of documents and developed a concept-based search engine system for engineering domain-specific technical documents. In recent years, ontologies have been used in retrieving BIM resources [7; 8; 9]. [PERSON] et al. [3] developed the domain ontology related to BIM-specific knowledge and provided an ontology-based query expansion approach to increase IR performance. Furthermore, [PERSON] et al. [6] combined natural language processing technology with the ontology-based query expansion method and achieved higher performance than traditional keyword retrieval technology in a BIM object database. As discussed above, in the construction industry, ontology technology has been applied to information retrieval. However, most papers investigated domain ontologies of building projects instead of non-building projects (e.g., bridges). The former ontologies cannot be directly applied to the latter because of the vast differences between them [24]. In the field of bridge engineering, some studies about ontology application [10; 11; 12; 25] have been conducted. However, these studies mainly focused on knowledge management [26], and seldom focused on information retrieval [10]. Table 1 presents existing ontologies in bridge engineering. It can be seen that the existing ontologies do not fully cover bridge inspection knowledge areas and most ontologies are created manually. For instance, [PERSON] et al. [11] manually modeled a BSHM ontology to conveniently retrieve sensory data. [PERSON] et al. [12] manually built a BrMontology and created a BrMontology-based knowledge base to assist engineers in making comprehensive decision. However, the BSHM ontology and BrMontology lack inspection-relevant information such as the spatial relationships among bridge elements, which would not be suitable for predicting the target element. [PERSON] et al. [27] created a bridge ontology (BridgeOnto) and the BridgeOnto includes the composition (part to whole) relations between bridge elements and the bridge. This work improves the performance of extracting bridge elements from bridge documents. However, the main limitation of BridgeOnto is that it does not contain the spatial relationships among bridge elements, which is important for correctly predicting the target element. A semi-automatic method [13] was developed to generate a bridge ontology from an Industry Foundation Classes (IFC) model. However, compared with the widely used BMSs, the emerging IFC models are not established for most bridges, which limits the application scope of this method. The above studies demonstrate the potential of ontology technology in improving the efficiency of information retrieval. However, there is still some work to be completed to improve the efficiency of bridge inspection. (1) The current ontology researches do not fully cover bridge inspection knowledge areas, and a new ontology model needs to be further constructed to support inspection-relevant information retrieval. Meanwhile, to alleviate the deficiency of hand-crafted ontology, a proposed improved seven-step method is to generate a bridge ontology by mapping relational data stored in the BMS rather than by parsing IFC models. This is the difference between our method and the method in the paper [13]. (2) The bridge ontology applications are still at the conceptual stage [12].