DogOnt as a viable seed for semantic modeling of AEC/FM

Bonino, Dario; Russis, Luigi De

doi:10.3233/sw-180295

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…When combined, these four ontologies align with W3C recommendations for adjacent domains, e.g. the geospatial ontologies [27], Smart Appliances REFerence 11 (SAREF) [28], Domotics intelligent device 12 (DogOnt) [29], and SSN [13]. Also, a Building Automation and Control Systems (BACS) ontology now supports monitoring and controlling functions to improve building operations in terms of energy efficiency and indoor comfort [30].…”

Section: Linking Cross-domain Building Information With Rdfmentioning

confidence: 99%

Building energy performance assessment using linked data and cross-domain semantic reasoning

Wang

Hoare

et al. 2021

Automation in Construction

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Section: Linking Cross-domain Building Information With Rdfmentioning

confidence: 99%

Building energy performance assessment using linked data and cross-domain semantic reasoning

Wang

Hoare

et al. 2021

Automation in Construction

View full text Add to dashboard Cite

show abstract

“…In the smart building domain, ontologies varied from the physical building, systems, devices, and governing rules [21,22] to including device users [23][24][25][26][27][28][29] and the energy providers [30]. The inclusion of the role of actors within the knowledge domain is applicable to the design of our ontology; however, these ontologies do not appear to capture the interactions (or relationships) between actors in the system.…”

Section: Related Ontologiesmentioning

confidence: 99%

Solar soft cost ontology: a review of solar soft costs

Beck

Rai

2019

Prog. Energy

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The continued deployment of renewable energy is critical to the energy transition necessary for climate change mitigation. Since cost is one of the primary drivers of solar adoption, we must continue to reduce the cost of solar to hasten the transition to clean energy. Currently, solar soft costs account for 52%-70% of the cost of an installed residential solar photovoltaic (PV) system in the U.S. These costs are persistent and an increasing share of the costs, since hardware costs continue to decline. The consensus on a definition of soft costs is 'non-hardware costs', but defining something by what it is not does not tell us what it is, which makes it difficult to study and understand. To this end, we developed the solar soft cost ontology (SSCO) to provide a comprehensive view of the complex and variable knowledge domain of solar soft costs. We identify the main categories of solar soft costs, how they relate to each other, how that knowledge is created, shared, and acquired, and the relevant actors in the solar soft cost knowledge ecosystem. Developed by coding a corpus of nearly 130 academic articles, the resulting ontology in web ontology language (OWL) includes 136 classes and 87 properties. The SSCO can facilitate and enhance knowledge transfer of research findings and best practices within and between stakeholder communities, including researchers, practitioners, and policy makers, that seek to address solar soft costs. We conclude with a discussion of potential applications and use cases for the SSCO among this diverse stakeholder community.

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“…The papers accepted in this special issue are representative examples of how Semantic Web technologies can be applied to make a better use of data models for the built en-vironment. Two papers focus on the energy efficiency of buildings and cities, namely Esnaola-Gonzalez et al [3] and Bonino and De Russis [1]. Building home automation is addressed by two papers from a perspective of a social network of agents (Ruta et al [8]) and an all-seeing smart building (Marroquin et al [4]).…”

Section: Special Issue Contributionsmentioning

confidence: 99%

“…In DogOnt as a viable seed for semantic modeling of AEC/FM, Bonino and De Russis [1] analyze the state of the art of ontologies for electrical and thermal consumption. In this review, authors conclude that current models address each level and domain separately, therefore preventing the representation of the energy domain in a structured and comprehensive way.…”

Section: Special Issue Contributionsmentioning

confidence: 99%

Semantic technologies and interoperability in the built environment

Pauwels

Poveda‐Villalón

Sicilia

et al. 2018

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The built environment consists of plenty of physical assets with which we interact on a daily basis. In order to improve not only our built environment, but also our interaction with that environment, we would benefit a lot from semantic representations of this environment. This not only includes buildings, but also large infrastructure (bridges, tunnels, waterways, underground systems), and geospatial data. With this special issue, an insight is given into the current state of the art in terms of semantic technologies and interoperability in this built environment. This editorial not only summarizes the content of the Special Issue on Semantic Technologies and interoperability in the Built Environment, it also provides a brief overview of the current state of the art in general in terms of standardisation and community efforts.

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DogOnt as a viable seed for semantic modeling of AEC/FM

Cited by 11 publications

References 19 publications

Building energy performance assessment using linked data and cross-domain semantic reasoning

Building energy performance assessment using linked data and cross-domain semantic reasoning

Solar soft cost ontology: a review of solar soft costs

Semantic technologies and interoperability in the built environment

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