Review Comment:
The authors present a work on collecting occupant feedback in buildings. The paper is written in good English. The problem addressed is that closed- and open-loop control of building services are based on sensor measurements, however, using this approach it is impossible to satisfy in all cases the individual comfort preferences of an occupant. The problem is well known and described in the literature by various authors. Traditionally occupants would complain their dissatisfaction to a facility manager, which would manually change setpoints in the automation system to (hopefully) improve the comfort level of users.
Hence, solutions as proposed in the paper are highly relevant that:
- Automatically and continuously collect occupant feedback in a scalable and convenient way and;
- Link the collected feedback to additional and/or existing building information.
The ultimate goal is that occupant feedback is integrated in a way, such that setpoints in the automation system automatically adapt to the users’ needs as he/she reports complaints.
In the abstract and introduction, the authors state the following contributions:
1. A scalable method to acquire continuous occupant feedback and directly integrate with other building information;
2. A formal model, the Occupant Feedback Ontology (OFO) to capture occupant feedback and directly integrate feedback with building information;
3. A smartwatch app, Mintal, which implements the ontological model and allows users to state their indoor environmental quality feedback;
4. The creation of occupant-centered digital twins that support decision making.
The approach presented using a smartwatch app to collect occupant feedback is a viable solution. The solution architecture (Fig. 2) presented in their use case allows for the scalable acquisition of occupant feedback. The aspect of “integrate with building information” is treated too vague throughout the paper and needs further clarification and thorough explanation:
- Which concepts and relationships are utilised to link to building information? How is actually building information obtained from source formats? The description in the use case indicates that for the studied building it was implemented manually. If the presented approach is supposed to be scalable, an automated solution for this task needs to be found.
- The ontology presented in chapter 5 and depicted in Fig. 3 shows a concept ofo:Location. In the query presented in Listing 12 “bot:hasElement” is utilised for linking to a bot:Element. Is this mismatch intended?
The formal model, the Occupant Feedback Ontology (OFO) is presented in chapter 5. The presented explanations and visualisation allow to understand the modelling choices. The authors define seven competency questions to evaluate their modelling and later present queries in SPARQL implementing these questions. The web-based documentation is accessible and is helpful to retrieve the ontology as well as find further explanations.
The ontology is well designed to fit the specific purpose of the use case. Unclear, is why in many cases known modelling patterns have been used but, new concepts have been defined. Well-known ontology engineering methods stipulate the reuse of existing ontologies (1), however, no explicit links could be found for concepts in OFO. Some concepts could for instance be mapped to:
- ofo:Person -> foaf:Person?
- ofo:Location -> bot:Space? Cf. (4)
- ofo:FeatureOfInterest -> sosa:FeatureOfInterest
- ofo:Datapoint -> brick:Point cf. (2)
- ofo:hasPropertyState -> opm:hasPropertyState cf. (3)
- ofo:Wearable -> s4wear:Wearable (https://saref.etsi.org/saref4wear/Wearable)
Moreover, the following questions remain open:
5. CQ3 -> “Location or object”: The created relationships in the ontology suggest to only referring to ofo:Location. How to relate to an object? Cf. also the quest in Listing 12 which relates to a bot:Element?
The app called Mintal presented in section 6 allows occupants to return feedback. The app immediately issues the generated data as RDF triples. Similar apps have been implemented and described by other authors cf. (5)(9). To assess the value of this contribution the authors should review existing solutions in more detail according to some criteria and clearly justify, what distinguished Mintal from existing apps.
Lastly, the notion of a “occupant-centered digital twin” is mentioned in the title and abstract, however it remains unclear what is meant by the authors. If the core focus of this paper relies in these topics, the authors should revise the manuscript accordingly.
In addition, in a couple of statements in the paper the authors claim that decision making is enhanced. However, it remains unclear what decision making is meant, and at which point in the building lifecycle. The authors should revise the manuscript to clarify on this.
Given the above evaluation I suggest performing a major revision of the paper. I would propose to revise the structure of the paper as follows:
- Introduction
- Related Work -> revise the content in Introduction and chapter 2 and 3 into one structured related work section. The outcome of this section should clearly state, why the development of OFO and Mintal is necessary
- OFO
- Mintal
- Use case including experimental setup
o Place here the content of chapter 4 and 7. In particular, section 7.8 use case seems to be misplaced at its current position
- Discussion
- Conclusion
A revision of this work should moreover focus on the actual content and clarifying on the novelty aspects of the presented contributions:
- What made the development of OFO necessary? Why not use existing ontologies presented in the past (BOT, OPM, HEX, SOSA, s4wear, …)
- What distinguishes the presented app and methodology from existing approaches, in particular to the contributions by:
o (6) (9) (10) (7)
- How does the work relate to the overall concept of a digital twin in the built environment?
For the readers of this journal, it would be important to know in more detail, what is actually the benefit of semantic web technologies in the presented use case? The authors state that “Semantic web technologies were applied to solve data interoperability issues”. It would be great to learn in more detail, which interoperability issues have been observed and how semantic web technologies helped to overcome these.
I am looking forward receiving your answer and the revised manuscript.
Best,
Georg
Some notes with minor comments I´d like to share.
Chapter 3 Occupant Feedback
1. While the authors present a comprehensive overview of a full body of literature a final conclusion and assessment is missing. What is the gap in existing ontologies that they are filling?
Conclusion
The conclusion narrative should be revised. It reads more like an introduction
2. “We found two challenges in the literature.” -> Please provide citations
3. “The literature mentions various issue” -> it would be helpful if you point to cited works here or to the related work section. It remains unclear to the reader, what is “the literature”
Please check throughout the whole paper that abbreviations are introduced once and used afterwards consistently.
o “First, we presented the Occupant Feedback Ontology. OFO “ -> E.g. “Occupant Feedback Ontology (OFO). OFO …”
o „The EMA method“
o „BOP ontology“
References:
Below references I have been referring to in this review:
(1) Pinto, H. S., & Martins, J. P. (2004). Ontologies: How can they be built? Knowledge and information systems, 6(4), 441-464.
(2) Balaji, B., Bhattacharya, A., Fierro, G., Gao, J., Gluck, J., Hong, D., ... & Whitehouse, K. (2016, November). Brick: Towards a unified metadata schema for buildings. In Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments (pp. 41-50).
(3) Holten Rasmussen, M., Lefrançois, M., Bonduel, M., Anker Hviid, C., & Karlshøj, J. (2018). OPM: An ontology for describing properties that evolve over time. In CEUR Workshop Proceedings (Vol. 2159, pp. 24-33). CEUR Workshop Proceedings.
(4) Rasmussen, M. H., Lefrançois, M., Schneider, G. F., & Pauwels, P. (2021). BOT: the building topology ontology of the W3C linked building data group. Semantic Web, 12(1), 143-161.
(5) Abdallah, M.; Clevenger, C.; Vu, T.; Nguyen, A. Sensing Occupant Comfort Using Wearable Technologies. In Proceedings of the 2016 Construction Research Congress, San Juan, Puerto Rico, 31 May–2 June 2016; pp. 940–950.
(6) Barrios, L.; Kleiminger, W. The Comfstat—Automatically sensing thermal comfort for smart thermostats. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), Kona, HI, USA, 13–17 March 2017, pp. 257–266.
(7) Ramsauer, D., Dorfmann, M., Tellioğlu, H., & Kastner, W. (2022). Human Perception and Building Automation Systems. Energies, 15(5), 1745.
(8) Qiu, H., Schneider, G. F., Kauppinen, T., Rudolph, S., & Steiger, S. (2018). Reasoning on Human Experiences of Indoor Environments using SemanticWeb Technologies. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 35, pp. 1-8). IAARC Publications.
(9) P. Jayathissa, M. Quintana, T. Sood, N. Nazarian, and C. Miller, Is your clock-face cozie? A smartwatch methodology for the in-situ collection of occupant comfort data, in: Journal of Physics: Conference Series, 2019. doi:10.1088/1742-6596/1343/1/012145.
(10) F.M. Gray, H. Dibowski, J. Gall, and S. Braun, Occupant Feedback and Context Awareness: On the Application of Building Information Modeling and Semantic Technologies for Improved Complaint Management in Commercial Buildings, in: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2020. doi:10.1109/ETFA46521.2020.9212164.
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