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An ontology for sensors knowledge management in intelligent manufacturing systems
Published in Paulo Jorge da Silva Bartolo, Fernando Moreira da Silva, Shaden Jaradat, Helena Bartolo, Industry 4.0 – Shaping The Future of The Digital World, 2020
M. Mabkhot, M. Krid, A. M. Al-Samhan, B. Salah
Since its emergence, ontology has been adopted in MSs in order to speed up manufacturing processes by facilitating access to knowledge encoded in a way that makes it reusable (Ramos 2015). Dasgupta & Dey (2013) suggested a semantic representation of sensor data. Eid, Liscano & El Saddik (2007) suggested an ontology to describe heterogeneous sensor knowledge. Corcho & García-Castro (2010) reviewed such works and discussed the need to interpret, manage, and integrate data derived from heterogeneous sensor networks in a meaningful way. W3C Semantic Sensor Network Incubator group (the SSN-XG) produced a Semantic Sensor Network (SSN) ontology that describes sensors in terms of capabilities, measurement processes, observations, and deployments. This ontology was later deployed in Dey, Jaiswal, Dasgupta, & Mukherjee (2016) to semantically present the energy meter as a sensor. The aim of the deployment was to use semantic technology to manage and improve the energy efficiency in a building.
Integration of an ontology with IFC for efficient knowledge discovery in the construction domain
Published in Jan Karlshøj, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2018
Z.S. Usman, J.H.M. Tah, F.H. Abanda, C. Nche
The Web Ontology Language (OWL) is the standard language for the ontology layer of the semantic web. It is recommended by the World Wide Web Consortium (W3C) (W3C, 2004). OWL executes great machine interoperability of the contents of the web. It specifies a collection of operators to develop concept definitions and descriptions as well as reasoners to perform consistency checking of the ontology. Ontology developers usually adopt one of OWL and OWL 2 (OWL2 EL, QL and RL) sublanguages which best suits the needs of the application. The expressive power, computational completeness, reasoning capacity and limitations of the OWL sublanguages are amongst the characteristics that are analyzed during selection. The level of expressiveness of the OWL language determines what is represented in the OWL ontology (Pauwels & Terkaj, 2016). The reader is referred to the W3C OWL recommendation document for more details (W3C, 2004). OWL and SWRL together perform extensive semantic reasoning (Chen & Luo, 2016). Thus an ontology provides standard conceptualization and the semantic knowledge reasoning required on the selected domain. In this study, the Photovoltaic (PV) Systems is the selected domain of interest.
SimpleBIM: From full ifcOWL graphs to simplified building graphs
Published in Symeon E. Christodoulou, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2017
An entirely distinct strategy to simplify BIM information models, is to entirely disregard the IFC standard and instead consider drastically simplified BIM ontologies. Several authors have suggested such ontologies. For example, Niknam and Karshenas (2015) proposed a ‘sumo’ shared ontology that only contains the key components of a building model (walls, spaces, elements, floors). Depending on the use case, this shared ontology is then expanded with data following a design ontology, an estimating ontology, and so forth. A much earlier example in which a separate ontology was built from scratch, aiming particularly to represent building knowledge in an ontology-based fashion, was proposed by Lima et al. (2003, 2005) as part of the e-Cognos project. This ontology describes four key elements in construction, namely actors, resources, processes and products. Many of the lessons learnt from the e-COGNOS project are documented in El-Diraby (2013), which presents a domain ontology for construction knowledge (DOCK 1.0) starting from the earlier e-COGNOS work (2005–2013). Similarly, Ruikar et al. (2007) proposed an extensible set of modular ontologies (design-process ontology and team profile ontology) which are then deployed in an ontology-based knowledge-sharing environment (OnToShare) for usage by various stakeholders in construction industry.
Group decision support for product lifecycle management
Published in International Journal of Production Research, 2021
Bart L. MacCarthy, Robert C. Pasley
How information is structured in a PLM system is important. Gruber (1995) describes an ontology as ‘an explicit specification of a conceptualisation’, which can be used to capture domain knowledge in terms of entities and their interrelationships. El Kadiri and Kiritsis (2015) reviewed ontology research in the context of PLM and identified seven key roles that information ontologies may fulfil in future PLM. They conclude that ontologies are central to solving data integration problems, with initiatives such as OntoSTEP being developed. Currently such efforts are focused on product data interoperability rather than activity-based interoperability (Lentes and Zimmermann 2017). However, there is little or no explicit discussion of the need to, or the processes by which decisions and their related information should be captured and recorded in PLM systems. We argue that PLM systems can benefit from such thinking.
Evaluating the environmental performance of pipeline construction using systems modelling
Published in Construction Management and Economics, 2020
Mohamed Matar, Hesham Osman, Maged Georgy, Azza Abou-Zeid, Moheeb Elsaid
The SBESEF framework has been validated by a number of experts in different domains due to its relatively uncommon nature in the field of construction. First, the SysML module representing the environmental system has been validated through expert interviews with a number of experts in the field of environmental impact assessment, who verified the results of the top-down and bottom-up analysis that represent the backbone of the model. Further on, the modules for open cut trenching and microtunneling were validated through expert interviews from both a major consultant in the field of pipeline design, and a major pipeline construction contractor in Egypt. Finally, the whole framework and NCWP case study findings have been verified again with the same EIA experts after producing the results. The verification of the case study did not involve comparison of outputs produced by the model with those produced by other frameworks due to the unavailability of other models that can calculate this level of detailed analysis. The case study verification involved verifying that the results of the model are within reasonable engineering limits through expert interviews has often been used in cases similar to the SBESEF framework. For example, El-Diraby and Osman (2011) used domain expert interviews to validate a domain ontology for construction concepts in urban infrastructure products.
Domain ontology development of knowledge base in cardiovascular personalized health management
Published in Journal of Management Analytics, 2019
Weiqiang Zhang, Yidan Xiang, Xiaohui Liu, Pengzhu Zhang
Although numerous researchers have carried out preliminary research on the application of domain ontology-based knowledge base in health management of CVD, current attempts are mainly focused on the field of diabetes. For example, Lee, Wang, and Hagras (2010) used domain ontology to construct a knowledge base and reasoning rule base of diet for diabetic patients. The diet ontology library constructed the composition and content database of six common food categories in Taiwan, based on which dietary ingredients were recommended. El-Sappagh and colleagues used domain ontology to perform a series of studies on the diabetes knowledge base. To utilize the medical information, a whole life cycle ontology engineering method was put forward, and knowledge intensive case base reasoning (KI-CBR) was achieved (El-Sappagh, El-Masri, Elmogy, Riad, & Saddik, 2014). In addition, utilizing ontology from collected SNOMED-CT concepts, an EHR case base was established (El-Sappagh, Elmogy, Riad, Zaghloul, & Badria, 2017). Fuzzy ontology was also applied to the creation of a knowledge base to help achieve subsequent case matching in their later research (El-Sappagh & Elmogy, 2017). Hempo, Arch-int, Arch-int, and Pattarapongsin (2015) established a knowledge base composed of a patient profile database, a disease sign and symptom database, a complications database, and a self-care database in the field of diabetes.