## 2 Related Work
### BIM Information Retrieval  
Among all these interaction methods, natural language-based voice interaction is widely recognized as the most instinctive communication method for humans, offering significant user-friendliness and efficiency advantages [18]. Researchers are working to develop models and algorithms that enable systems to understand user queries and extract relevant information from BIM data. These systems typically employ techniques such as text understanding, information retrieval, and question-answering [19]. This approach allows users to express their needs and intentions in everyday language without requiring the learning of specialized command syntax, thereby facilitating efficient communication with BIM systems. This interaction method not only lowers the technical barriers for non-professional users but also significantly enhances the convenience and accuracy of information retrieval, opening broad prospects for promoting and applying BIM technology and the construction industry's digital transformation. Some studies also involve implementing intelligent reasoning and feedback in BIM knowledge question-answering systems [7]. No matter what kind of interaction method, the critical point is retrieving BIM model information. Conventionally, there are three ways: keyword-based retrieval and matching, ontology-based methods, and rule-based methods. Keyword-based retrieval methods locate and extract relevant entities or attributes within BIM models by identifying key terms within user queries. [PERSON] et al. proposed the concepts of \"keywords\" and \"constraints\" to capture key objects of user interest and designed an improved IFD-based mapping method from keywords to IFC entities or attributes [1]. [PERSON] et al. also proposed a framework for solving intelligent BIM data mining problems using natural language processing (NLP) and the International Framework for Dictionaries (IFD) [20]. [PERSON] et al. utilized natural language processing techniques and IFD technology to parse and unify queries [2]. [PERSON] et al. proposed a framework that employs a support vector machine (SVM) algorithm to determine the type of user question, utilizes NLP for syntactic analysis, identifies the main keywords of the user's question, and then uses ontology databases such as IFCOWL and NLP methods (latent semantic analysis LSA) for semantic understanding of the problem [21]. [PERSON] et al. used IFD to perform semantic disambiguation on key information processed by NLP, thereby obtaining standardized natural language word sequences [22]. Keyword-matching methods can have fast responses. However, they may face ambiguous keyword problems; thus, the performance relies on accurately identifying the keywords. Ontology-based methods are proposed to handle the keyword's ambiguous issues. [PERSON] et al. mapped standardized concepts and synonyms obtained from an IFD-based domain ontology through an NLP parser to an IFC-based BIM object database, sorted the results based on a similarity threshold, and obtained the final result [23]. [PERSON] et al. developed a modular IFC Natural Language Expression (INLE) ontology that uses ontology-based instance filling and text parsing to extract expected variables and different levels of constraints from NLQ and convert them into standard SPARQL queries that can be used to retrieve IFC-based BIM models [24]. [PERSON] et al. proposed an online BIM resource automatic query extension method based on IFC IR ontology and local context analysis (LCA) [25]. Ontology-based methods enhanced the robustness of BIM information retrieval procedures. However, constructing an ontology model relies on experts' experience; thus, building costs are relatively high. A compromise is a rule-based method. [PERSON] et al. used vectorization to locate the possible IFC entity with the highest cosine similarity. Once a possible IFC entity was found, corresponding natural language paragraphs were generated from the IFC entity based on the IFC4 pattern [2].