While the adoption of open Building Information Modeling (open BIM) standards continues to grow, the inherent complexity and multifaceted nature of the built asset lifecycle data present a critical bottleneck for effective information retrieval. To address this challenge, the research community has started to investigate advanced natural language-based search for building information models. However, the accelerated pace of advancements in deep learning-based natural language processing research has introduced a complex landscape for domain-specific applications, making it challenging to navigate through various design choices that accommodate an effective balance between prediction accuracy and the accompanying computational costs. This study focuses on the semantic tagging of user queries, which is a cardinal task for the identification and classification of references related to building entities and their specific descriptors. To foster adaptability across various applications and disciplines, a semantic annotation scheme is introduced that is firmly rooted in the Industry Foundation Classes (IFC) schema. By taking a comparative approach, we conducted a series of experiments to identify the strengths and weaknesses of traditional and emergent deep learning architectures for the task at hand. Our findings underscore the critical importance of domain-specific and context-dependent embedding learning for the effective extraction of building entities and their respective descriptions.

虽然开放式建筑信息模型（开放式 BIM）标准的采用不断增长，但建筑资产生命周期数据固有的复杂性和多面性给有效的信息检索带来了关键瓶颈。为了应对这一挑战，研究界已开始研究基于自然语言的高级搜索来构建信息模型。然而，基于深度学习的自然语言处理研究的进步步伐加快，为特定领域的应用带来了复杂的环境，使得通过各种设计选择来适应预测准确性和伴随的计算成本之间的有效平衡变得具有挑战性。本研究重点关注用户查询的语义标记，这是识别和分类与建筑实体及其特定描述符相关的参考文献的一项基本任务。为了促进跨各种应用程序和学科的适应性，引入了牢固植根于行业基础类 (IFC) 架构的语义注释通过采用比较方法，我们进行了一系列实验，以确定传统和新兴深度学习架构对于当前任务的优势和劣势。我们的研究结果强调了特定领域和上下文相关的嵌入学习对于有效提取构建实体及其各自的描述的至关重要性。