A fuzzy extension of the semantic Building Information Model

Gómez‐Romero, Juan; Bobillo, Fernando; Ros, M. Blanca; Molina-Solana, Miguel; Ruiz, M. Dolores; Martín-Bautista, María J.

doi:10.1016/j.autcon.2015.04.007

Cited by 35 publications

(18 citation statements)

References 34 publications

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“…Park et al [28] and Ding et al [29] applied ontology-based knowledge representation to aid knowledge retrieval under the BIM environment. To implicitly or imprecisely retrieve knowledge, Gómez-Romero et al [30] combined fuzzy description logics with ontology in the BIM-based system to improve the knowledge retrieval results, in which the retrieval mechanism is not either "true or false" but relatively holds the truth. Additionally, case-based reasoning is introduced in the BIM environment for knowledge storage and retrieval [25].…”

Section: Bim-based Kmmentioning

confidence: 99%

Involving knowledge of construction and facilities management in design through the BIM approach

Wang¹,

Meng²,

McGetrick³

2018

Creative Construction Conference 2018 - Proceedings

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The construction industry has increasingly realised the importance of knowledge. Accordingly, various strategies and tools have been applied over the years to support knowledge management (KM). In particular, building information modelling (BIM) is a technology that has recently emerged in the construction industry. BIM is an object-oriented and parametric-based tool with the features of digital visualisation, life cycle simulation, coordination and collaborative environment. Consequently, many studies have been conducted to explore these four aspects. However, existing studies on BIM-based management mainly focus on the information level. By contrast, only a few studies have explored KM under the BIM environment. Therefore, this study explores the potential and expectations of BIM-based KM for the early application of knowledge of construction and facilities management (FM) into the design stage. A total of 30 semi-structured interviews are conducted to collect qualitative information from the AEC industry. The existing KM practice is explored based on the analysis of the collected qualitative information. Thereafter, a discussion is presented on how the BIM-based KM can be used to mitigate the current KM challenges. Lastly, this study presents the expectations on BIMbased KM for involving the knowledge of construction and FM into the design phase. Overall, this study provides new insights into the transformation of research focus from BIM-based information management to BIM-based KM.

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Section: Bim-based Kmmentioning

confidence: 99%

Involving knowledge of construction and facilities management in design through the BIM approach

Wang¹,

Meng²,

McGetrick³

2018

Creative Construction Conference 2018 - Proceedings

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“…The fuzzy ontology was used to share and reuse knowledge between traffic intelligent transportation systems in Zhai et al (2007Zhai et al ( , 2008. Gómez-Romero et al (2015) presented a fuzzy logic-based extension of the semantic Building Information Models (BIMs) that provide support for imprecise knowledge representation and retrieval. A framework of building a fuzzy ontology for representing the meteorological knowledge was proposed by Truong et al (2011), and a method for fuzzy ontology integration is introduced for solving inconsistency among weather services' knowledge.…”

Section: Knowledge Representation and Reasoningmentioning

confidence: 99%

A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

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Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains, in this paper, we provide a comprehensive overview of fuzzy ontologies. In detail, we first introduce fuzzy ontologies from the most common aspects such as representation (including categories, formal definitions, representation languages, and tools of fuzzy ontologies), reasoning (including reasoning techniques and reasoners), and applications (the most relevant applications about fuzzy ontologies). Then, the other important issues on fuzzy ontologies, such as construction, mapping, integration, query, storage, evaluation, extension, and directions for future research, are also discussed in detail. Also, we make some comparisons and analyses in our whole review.ontology definition languages, such as RDF(S), SHOE, OIL, DAML, DAML + OIL, and web ontology language (OWL), have been developed over the past years (Horrocks et al., 2003). The OWL (Smith et al., 2004) and its successor OWL 2 (Cuenca Grau et al., 2008) is the W3C recommended ontology representation language. The theoretical underpinnings of OWL and ontologies are strongly based on Description Logics (DLs), a subset of first-order logic especially suitable for representing structured knowledge (Horrocks & Sattler, 2001;Baader et al., 2003;Horrocks et al., 2003).Although ontology is a quite expressive formalism, it features limitations, mainly with what can be said about fuzzy information. Nowadays, in ontology-based and many applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications of the Semantic Web, such as knowledge management, e-commerce, and web portals. For example, a task like a 'doctor appointment' could look like: 'Make me an appointment with a doctor close to my home not too early and of good references' (Stoilos et al., 2010). The conceptual formalism supported by typical ontology may not be sufficient to represent such information and knowledge. Therefore, many proposals have attempted to apply different formalisms...

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“…This lack of precision highlights the advisability of using techniques such as fuzzy logic, which can model the information more satisfactorily than conventional tools that can only process structured data. An interesting proposal in that regard is that of the fuzzy extension of Building Information Models [46]. Another characteristic in the context of Big Data is that data are mostly irrelevant in isolation.…”

Section: Uncertainty and Imprecisionmentioning

confidence: 99%

“…Meléndez et al [45] used pattern recognition techniques to identify sequences of electrical events in substations associated to major failures. This analysis process can be enhanced if sensor data is enriched with contextual information, like building semantic data [46] and cross-dependence databases [47]. In this case, data fusion techniques provide support for data alignment, event detection, and situation assessment.…”

Section: Fault Detection and Preventionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

228

108

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The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

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A fuzzy extension of the semantic Building Information Model

Cited by 35 publications

References 34 publications

Involving knowledge of construction and facilities management in design through the BIM approach

Involving knowledge of construction and facilities management in design through the BIM approach

A survey on fuzzy ontologies for the Semantic Web

Data science for building energy management: A review

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