China
Africa
Explore chapters and articles related to this topic
Building product models, terminologies, and object type libraries
Published in Pieter Pauwels, Kris McGlinn, Buildings and Semantics, 2023
Aaron Costin, Jeffrey W. Ouellette, Jakob Beetz
One such language that incorporates RDF is the Web Ontology Language (OWL) . There are three levels of OWL: OWL Lite, OWL DL (Description Logic), and OWL Full. The simplest level, OWL Lite, supports only a subset of the OWL language constructs, and provides a classification hierarchy and simple constraints. OWL Lite is used by users who want to support OWL full, but want to start at a basic level. In addition to rules and requirements of OWL Lite, OWL DL adds the tools and features of Description Logic to represent the relations between objects and their properties. Description Logic, the basis of any ontology language, is the formal knowledge representation used to express the conceptualisation of domains in an organised and formally well-understood manner. OWL Full provides the highest freedom of using the OWL language and RDF constructs, but takes considerably more computing power to run the inference engines. The current release, OWL 2 (Figure 1.6), can be found in the W3C standards pages.2 Additionally, the Rule Interchange Format (RIF) defines a standard for exchanging rules among systems on the Web that specifies how RDF, RDFS, and OWL interrelate.
Modular concatenation of reference damage patterns
Published in Jan Karlshøj, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2018
Since the primarily focus of the RDP ontology is not the creation of detailed damage models but the structuring and modular concatenation of predefined defects it makes sense to transform the retrieved Elementary Damages into an appropriate data model. In order to utilize the aforementioned concept for computer-aided modeling and evaluation of damage a machine-readable format must be used for serialization. Open data exchange formats such as the Extensible Markup Language (XML), the JavaScript Object Notation (JSON) or even the standardized BIM format, the Industry Foundation Classes (IFC) are suitable for serializing information which can be processed by software applications. Nevertheless, for supporting semantic reasoning and storing knowledge data, as aimed by this research, a format for defining ontologies is preferable. Therefore, several languages for describing ontologies exist, e.g. the Knowledge Interchange Format (KIF), Resource Description Framework (RDF) or the Web Ontology Language (OWL). The structure of RDPs in this research is based on ontologies formalized in OWL (W3C, 2012) which according to (Kalibatiene, et al., 2011) claims to be the most popular, spreading and applicable ontology language.
Scalable Signal Data Processing for Measuring Functional Connectivity in Epilepsy Neurological Disorder
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
Arthur Gershon, Samden D. Lhatoo, Curtis Tatsuoka, Kaushik Ghosh, Kenneth Loparo, Satya S. Sahoo
Terminological heterogeneity in data generated from multiple sources arises due to the use of disparate terms to describe similar physiological events (e.g., signal complexes in EEG recordings), and it represents a key challenge in integrating large-scale neuroscience data [1]. To address this critical challenge, we use terms modeled in existing biomedical ontologies to annotate signal data in CSF files as part of the computational pipeline. Ontologies are knowledge models that represent terms in a domain of discourse using formal knowledge representation languages, such as the description logic-based Web Ontology Language (OWL2) [43]. Biomedical ontologies have been widely adopted and used to reconcile data heterogeneity and support data integration and querying. For example, Gene Ontology (GO) is widely used to annotate genomic data to facilitate the use of common terminology across different data sources and also enable users to easily query the integrated data [44].
The emergence of cognitive digital twin: vision, challenges and opportunities
Published in International Journal of Production Research, 2022
Xiaochen Zheng, Jinzhi Lu, Dimitris Kiritsis
Semantic technologies have been used as key enabling components in many intelligent systems to achieve semantic interoperability for heterogeneous data and information (Cho, May, and Kiritsis 2019; Psarommatis 2021). Semantic models enable to capture system information in an intuitive way and to provide a concise and unified description of such information. They describe the information in standardised ontology languages making it possible to specify direct interrelationships among various systems and models (Kharlamov et al. 2018). As an advanced semantic technology, knowledge graph enables to describe model information in the form of entities and relationships, which makes it possible of creating new knowledge using a reasoner (Ehrlinger and Wöß 2016; Nickel et al. 2015). This makes semantic modelling and knowledge graph modelling promising solutions for integrating heterogeneous DT models involved in a complex system across different domains and lifecycle phases.
Application of the ‘Surgical GPS’ to posterior spinal fusion procedures for scoliosis correction
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Austin Tapp, James Bennett, Michel. A. Audette
To incorporate SPMs for patient-specific interventions, it is necessary to employ textual syntax resource description framework (RDF) files called a Terse Resource Description Framework Triple Language (TURTLE), which enables ontological data exchange through innate semantic web capabilities (Dunn and Markoff 2009; Herman and Web 2015; Berners-Lee and Jaffe 2020). While an RDF is traditionally used to process metadata and provide interoperability between applications that exchange machine-comprehensible information, ontologies, in Web Ontology Language (OWL), are a common framework for the semantic web, and are understood to be a formal collection of terms. Subsequently, TURTLEs provide levels of compatibility within the N-Triples format and the triple pattern of SPARQL (SPARQL Protocol and RDF Query Language) to encompass the benefits of both ontologies and RDF files (Lassila and Swick 2020; Noy et al. 2001; Guarino et al. 2004). A TURTLE represents the RDF template in a compact textual form that is physician-readable while maintaining its ontologies strict conceptualisations (Fetzer et al. 2016). TURTLEs have the distinct triple format that follows <subject> <predicate> <object> (Lassila and Swick 2020). In the scope of this study, a single triple might be ‘pedicleScrews’ ‘implantedOn’ ‘pedicles”, ’where the <object>, “pedicles”, is‘a specif’cally designated landmark denoted by the physician.
An overview of current technologies and emerging trends in factory automation
Published in International Journal of Production Research, 2019
Mariagrazia Dotoli, Alexander Fay, Marek Miśkowicz, Carla Seatzu
Regarding Web Semantics, the Web Ontology Language (OWL), which is based on the eXtensible Markup Language (XML) and the Resource Description Format (RDF), can be applied to semantically describe all objects of interest and their semantic relationships. Important semantic relationships used by Lastra and Delamer (2006) are ‘isA’ (is a type of) or ‘hasSkill’ (can perform process). Subsequently, various needs to connect systems via ontologies, including those which impose the requirements R3, R9, R19 and R20, have been addressed by researchers. In (Kalogeras et al. 2006), enterprise process workflows are described with an ontology. This allows decoupling of the workflow from the Web Services required for its execution, which again provides a means to dynamically adopt to variations or changes of enterprise and industrial information systems. Therani (2007) shows how ontologies can support the product lifecycle management in an environment where physical items carry information about their past and their future needs with themselves, according to the IoT framework (Xiao et al. 2014). In combination with Cloud technology (see Section 2), the semantic knowledge of distributed automation systems can be enhanced and shared, as shown in (Stenmark et al. 2015) at the example of mobile robots.