对比四大建筑语义框架,发现无通用方案可实现智能建筑数据互通。
A Systematic Comparison and Evaluation of Building Ontologies for Deploying Data-Driven Analytics in Smart Buildings
- 构建框架从结构与实例两方面系统评估四个主流建筑语义模型。
- Brick Schema 和 RealEstateCore 在概念覆盖和表达能力上表现更优。
- 研究揭示语义差异是跨系统数据融合的核心障碍,适合建筑信息集成研究者参考。
语义模型在智能建筑应用中对数据交换、信息整合与知识共享至关重要。然而,现有主流建筑语义模型间的语义差异阻碍了数据互操作性,并限制了其在真实场景中的复用。本文提出并采用一个框架,从公理化设计(TBox)与实例断言(ABox)两个层面,对四种流行建筑语义模型(Brick Schema、RealEstateCore、Project Haystack、Google's Digital Buildings)进行系统比较与评估。在TBox评估中,基于SQuaRE的语义模型质量评价(OQuaRE)框架显示,Project Haystack 和 Brick Schema 在公理设计上更为紧凑。在ABox评估中,通过实际建筑数据的实证研究发现,Brick Schema 和 RealEstateCore 在捕捉建筑领域主要概念与关系方面具有更高的完整性和表达力。结果表明,尚无通用建筑语义模型可用于集成关联建筑数据(Linked Building Data, LBD)。本文进一步探讨语义模型兼容性,提出建筑语义模型设计模式(ODPs),以支持模型匹配、对齐与调和。
原文摘要 · Abstract (English)
Ontologies play a critical role in data exchange, information integration, and knowledge sharing across diverse smart building applications. Yet, semantic differences between the prevailing building ontologies hamper their purpose of bringing data interoperability and restrict the ability to reuse building ontologies in real-world applications. In this paper, we propose and adopt a framework to conduct a systematic comparison and evaluation of four popular building ontologies (Brick Schema, RealEstateCore, Project Haystack and Google's Digital Buildings) from both axiomatic design and assertions in a use case, namely the Terminological Box (TBox) evaluation and the Assertion Box (ABox) evaluation. In the TBox evaluation, we use the SQuaRE-based Ontology Quality Evaluation (OQuaRE) Framework and concede that Project Haystack and Brick Schema are more compact with respect to the ontology axiomatic design. In the ABox evaluation, we apply an empirical study with sample building data that suggests that Brick Schema and RealEstateCore have greater completeness and expressiveness in capturing the main concepts and relations within the building domain. The results implicitly indicate that there is no universal building ontology for integrating Linked Building Data (LBD). We discuss ontology compatibility and investigate building ontology design patterns (ODPs) to support ontology matching, alignment, and harmonisation.
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