A Systematic Comparison and Evaluation of Building Ontologies for Deploying Data-Driven Analytics in Smart Buildings

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 also discuss ontology compatibility and investigate building ontology design patterns (ODPs) to support ontology matching, alignment, and harmonisation.

💡 Research Summary

The paper addresses the pressing need for semantic interoperability in smart‑building data ecosystems by systematically comparing four widely‑used building ontologies: Brick Schema, RealEstateCore (RECore), Project Haystack, and Google’s Digital Buildings (DB). The authors adopt a two‑pronged evaluation framework that examines both the terminological (TBox) and assertion (ABox) layers of each ontology.

For the TBox assessment, they employ the SQuaRE‑based Ontology Quality Evaluation (OQuaRE) framework, which maps a hierarchy of quality characteristics (Structural, Functional Adequacy, Maintainability, Transferability, Reliability, Compatibility, Operability) to measurable metrics, normalizes scores, and aggregates them into an overall quality index. The analysis reveals that Haystack and Brick are the most compact in terms of class and property count, hierarchy depth, and lack of redundancy, indicating superior design compactness. RealEstateCore, while richer in domain concepts, scores lower on maintainability and transferability due to its larger, more complex schema. Google Digital Buildings, still in an early release, lags behind on most metrics.

The ABox evaluation uses an empirical case study of a medium‑size office building in Melbourne (3 storeys, 69 rooms, 18 HVAC zones). Real‑world sensor, set‑point, command, and alarm data (total 1,133 entities in the original Brick model) are mapped to each ontology. Mapping rules handle synonym classes, cross‑ontology equivalents, and fallback to super‑classes when exact matches are unavailable. Results show that Brick achieves the highest coverage (~95 %) across building spaces, equipment, and measurement points, demonstrating strong expressiveness and completeness. RealEstateCore performs especially well for HVAC equipment and system modeling, reflecting its detailed mechanical‑systems focus. Haystack’s tag‑centric approach yields lower coverage for complex relational structures, and Digital Buildings, being nascent, provides the least comprehensive representation.

The authors discuss ontology compatibility issues arising from divergent abstraction levels, differing validation languages (OWL vs. SHACL), and query paradigms (type‑based vs. tag‑based). To mitigate these challenges, they propose a set of Building Ontology Design Patterns (ODPs) that capture common concepts (spaces, equipment, points) and identify conflicting patterns, offering guidance for ontology matching, alignment, and harmonization. The paper emphasizes that no single ontology can satisfy all smart‑building analytics requirements; instead, a hybrid or layered approach, informed by quantitative TBox scores and empirical ABox coverage, is advisable.

In conclusion, the study provides a reproducible methodology for ontology selection, demonstrates that Brick and RealEstateCore together cover most practical needs, and highlights the necessity of ODP‑driven integration strategies. Future work is suggested on scaling the evaluation to more ontologies, automating the mapping process, and developing a standardized meta‑model to facilitate seamless Linked Building Data (LBD) integration across heterogeneous semantic resources.