# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 4 Critical Review
### Rule Interpretation
#### 4.2.2 Semantic Web  
With the development of the Semantic Web, ontology as the core layer has become a popular method for information modeling. Originating from the philosophical systems of interpreting existence, it represents a formal specification of shared concepts [178]. Ontology is applicable to modeling rules because the classes and properties in ontology models can easily represent the basic concepts and logics in the rules. Furthermore, semantic reasoning based on the ontology, rather than basic syntax, can achieve high-quality retrieval and matching of the rules. The use of ontology can enhance the integration and reuse of structured information, thereby improving the efficient interaction of different systems without misunderstanding and data loss [13].  
The domain ontology focusing on a specific field offers more practicality and professionalism. It includes concepts, entities, relationships, and properties aimed at expressing domain knowledge, providing a common understanding of the specific knowledge required for communication between humans and machines [179]. Since the domain knowledge is continually evolving and expanding, the methods of representing knowledge should be adjusted according to the problems to be solved. Ontology development should focus on how to capture the most important concepts in a structured and scalable format, rather than blindly pursuing the coverage. Therefore, determining the purpose and scope is crucial when starting to create an ontology model.  
In BMC, the primary purpose of domain ontology is knowledge modeling. Several ontologies have already been created to address specific problems [90; 91]. On one hand, ontology models can express the semantics of rules with strong logic of building standards. On the other hand, the structure of ontology models is extensible and flexible, adapting to the building standards that are frequently updated or modified. By establishing mappings between the building standards and the specific ontologies, the rules that need to be updated can be adaptively corrected. Additionally, there are commonalities between different professions or projects in the AEC field, allowing for the sharing and reuse of the similar ontologies to reduce the cost of rule expression.  
Information extraction aims to obtain useful information from the corpora to support rule expression and matching [51; 97]. Compared to the syntax-based text extraction, semantic extraction based on the domain ontology is more challenging, but the information obtained is more logical and valuable. This approach to information extraction maps the extracted semantic features to concepts and relationships in the ontology models. The implementation methods can be divided into rule-based and machine learning-based categories. The former requires domain experts to develop explicit rules and matching methods based on professional knowledge, which incurs higher initial preparation costs but leads to more accurate results. The latter relies on machine learning algorithms to train and optimize the models based on the sample data, automatically obtaining semantic features.