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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
In computer and information science, an ontology is an explicit specification of a conceptualisation, in which conceptualisation refers to the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold among them [157]. An ontology is the formal classification of entities in a particular domain that includes the types, properties, relationships, and other significant attributes about the entities within the domain. Ontologies are used to define the logic and semantics needed for computer systems and software applications. In other words, an ontology can be viewed as the foundation, or sublevel, needed to support computer systems. A taxonomy and ontology are very similar (see Figure 1.2a), and, in a non-technical sense, can be difficult to distinguish. The major difference is that the taxonomy is the classification structure, and the ontology contains the information and properties about those terms (see Figure 1.2b). In essence, a taxonomy is often a subpart of an ontology.
Ontologies
Published in Weiming Shen, Douglas H. Norrie, Jean-Paul A. Barthès, Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing, 2019
Weiming Shen, Douglas H. Norrie, Jean-Paul A. Barthes
An interesting use of an ontology is in the building of artificial systems, like databases, knowledge-based systems, or natural language understanding systems. Indeed, if we are able to process an unambiguous agreed-upon structure of concepts, adding labels to define a representation language is an easy task. In addition, the ontology provides the means to translate between the internal machine representation and the external natural language. The ontology gives a semantic foundation to the representation language. If in any specific domain we do not have an ontology to start with, the first step will be to develop one through a careful analysis of the domain, extracting the main concepts and selecting the appropriate vocabulary (which happens also to be good software engineering practice).
Standard Ontologies and HRI
Published in Paolo Barattini, Vicentini Federico, Gurvinder Singh Virk, Tamás Haidegger, Human–Robot Interaction, 2019
Sandro Rama Fiorini, Abdelghani Chibani, Tamás Haidegger, Joel Luis Carbonera, Craig Schlenoff, Jacek Malec, Edson Prestes, Paulo Gonçalves, S. Veera Ragavan, Howard Li, Hirenkumar Nakawala, Stephen Balakirsky, Sofiane Bouznad, Noauel Ayari, Yacine Amirat
In computer science and related technology domains, an ontology is considered a formal and explicit specification of a shared conceptualization [SBF98]. The conceptualization specified by an ontology, according to this point of view, encompasses the set of concepts related to the kinds of entities that are supposed to exist in a given domain, according to a community of practitioners. Thus, the main purpose of an ontology is to capture a common conceptual understanding about a given domain. Due to this, ontologies can be used for promoting the semantic interoperability among stakeholders, since sharing a common ontology is equivalent to sharing a common view of the world. Moreover, because ontologies specify the domain conceptualization in a formal and explicit way, this ensures that the meaning of every concept is rigorously specified and can be analyzed by humans and machines. Therefore, an ontology could be used as a common basis for communication between humans and machines. Finally, ontologies can also be viewed as reusable components of knowledge, since they capture the knowledge about a domain in a task-independent way. Thus, considering that the development of knowledge models is a notoriously difficult, time-consuming and expensive process, the adoption of ontologies promotes a more rational use of the resources in a project.
WMO: an ontology for the semantic enrichment of wetland monitoring data
Published in International Journal of Digital Earth, 2023
Xin Xiao, Hui Lin, Chaoyang Fang
Ontology, a key technology of the Semantic Web, is a very promising approach to reduce the semantic gap between data. Ontology is widely used to resolve the challenges of heterogeneity, interoperability, and complexity of data in integrated domains such as geospatial (Sun et al. 2019), urban (Kuster, Hippolyte, and Rezgui 2020), and disaster (Scheuer, Haase, and Meyer 2013). The numerous applications in a variety of domains show that ontology is a useful format for representing data because it can transform data from machine-readable to machine-understandable (Dubey, Patel, and Jain 2021). As a top-tier technology, ontology integrates, shares, and uses data to unify data models, improve data quality by metadata, support data mining and analysis, and promote knowledge sharing. The ontology should thus improve data interoperability and provide more reliable and effective support for data application and decision-making; it should also produce good results for wetland monitoring.
Foundations for a Fission Battery Digital Twin
Published in Nuclear Technology, 2022
Jeren Browning, Andrew Slaughter, Ross Kunz, Joshua Hansel, Bri Rolston, Katherine Wilsdon, Adam Pluth, Dillon McCardell
Deep Lynx does not provide version control or more complex editing tools for the standard model, so it is ideal if a project using Deep Lynx manages the standard model externally. For this effort, the team uses the Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND) ontology.11 An ontology is a collection of concepts and their relationships for a given domain. DIAMOND centers on the nuclear domain, and it contains classes, their properties, and relationships that describe this domain. Ontologies are graphs (classes/nodes related by relationships/edges); therefore, DIAMOND can be imported into Deep Lynx to be used as the standard model. Importing an ontology in this way creates a new container within Deep Lynx, a logical separation of data within an instance of Deep Lynx. Altering DIAMOND, as necessary, when integrating new applications and data to Deep Lynx is often the first step in development when building a Deep Lynx adapter application, such as the ML adapter, MOOSE adapter, or data historian adapter.
Development and evaluation of knowledge treasure for emergency situation awareness
Published in International Journal of Computers and Applications, 2021
In this step, we transcribe the concepts into OWL documents representing the ontology. Various ontology development tools are available such as Protégé, Apollo, and many more. We choose Protégé 5.2 to implement the domain ontology. It is an open source [11] and provides a plug-and-play environment for rapid application development. Protege is a most familiar tool to develop the ontology. Developers can create the ontology easily through its GUI without thinking about syntax language. Classes represent the concepts and properties represent the relationship between concepts. There are two types of properties Object properties and Data properties. Object properties define the relationship between classes. Data properties define the relationship between classes and values. Figure 3 shows the classification of concepts. Figure 4 shows the object and data properties. Both properties are used to store the relations in the ontology. Object properties link instances while data properties link instances to data.