Ontologies for Modeling and Simulation: Issues and Approaches (original) (raw)
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Ontology for modeling and simulation
Simulation Conference (WSC), …, 2010
This paper establishes what makes an ontology different in Modeling and Simulation (M&S) from other disciplines, vis-a-vis, the necessity to capture a conceptual model of a system in an explicit, unambiguous, and machine readable form. Unlike other disciplines where ontologies are used, such as Information Systems and Medicine, ontologies in M&S do not depart from a set of requirements but from a research question which is contingent on a modeler. Thus, the semiotic triangle is used to present that different implemented ontologies are representations of different conceptual models whose commonality depends on which research question is being asked. Ontologies can be applied to better capture the modeler ¶V perspective. The elicitation of ontological, epistemological, and teleological considerations is suggested. These considerations may lead to better differentiation between conceptualizations, which for a computer are of importance for use, reuse and composability of models and interoperability of simulations. 1 643 978-1-4244-9864-2/10/$26.00 ©2010 IEEE
Ontology–based Representation of Simulation Models
Ontologies have been used in a variety of domains for multiple purposes such as establishing common terminology, organizing domain knowledge and describing domain in a machine-readable form. Moreover, ontologies are the foundation of the Semantic Web and often semantic integration is achieved using ontology. Even though simulation demonstrates a number of similar characteristics to Semantic Web or semantic integration, including heterogeneity in the simulation domain, representation and semantics, the application of ontology in the simulation domain is still in its infancy. This paper proposes an ontology-based representation of simulation models. The goal of this research is to facilitate comparison among simulation models, querying, making inferences and reuse of existing simulation models. Specifically, such models represented in the domain simulation engine environment serve as an information source for their representation as instances of an ontology. Therefore, the ontology-based representation is created from existing simulation models in their proprietary file formats, consequently eliminating the need to perform the simulation modeling directly in the ontology. The proposed approach is evaluated on a case study involving the I2Sim interdependency simulator.
Using ontologies for simulation integration
2007 Winter Simulation Conference, 2007
This paper describes the motivations, methods, and solution concepts for the use of ontologies for simulation model integration. Ontological analysis has been shown to be an effective initial step in the construction of intelligent systems.
Ontologies for modeling and simulation: An initial framework
2007
Many fields have or are developing ontologies for their subdomains. The Gene Ontology (GO) is now considered to be a great success in biology, a field that has already developed several extensive ontologies. Similar advantages could accrue to the Modeling and Simulation community. Ontologies provide a way to establish common vocabularies and capture domain knowledge for organizing the domain with a community-wide agreement.
From domain ontologies to modeling ontologies to executable simulation models
2007
Ontologies allow researchers, domain experts, and software agents to share a common understanding of the concepts and relationships of a domain. The past few years have seen the publication of ontologies for a large number of domains. The modeling and simulation community is beginning to see potential for using these ontologies in the modeling process. This paper presents a method for using the knowledge encoded in ontologies to facilitate the development of simulation models. It suggests a technique that establishes relationships between domain ontologies and a modeling ontology and then uses the relationships to instantiate a simulation model as ontology instances. Techniques for translating these instances into XML based markup languages and then into executable models for various software packages are also presented.
Guidelines for Developing Ontological Architectures in Modelling and Simulation
Ontology, Epistemology, and Teleology for Modeling and Simulation: Philosophical Foundations for Intelligent M&S Applications
This book is motivated by the belief that “a better understanding of ontology, epistemology, and teleology” is essential for enabling Modelling and Simulation (M&S) systems to reach the next level of ‘intelligence’. This chapter focuses on one broad category of M&S systems where the connection is more concrete; ones where building an ontology – and, we shall suggest, an epistemology – as an integrated part of their design will enable them to reach the next level of ‘intelligence’. Within the M&S community, this use of ontology is at an early stage; so there is not yet a clear picture of what this will look like. In particular, there is little or no guidance on the kind of ontological architecture that is needed to bring the expected benefits. This chapter aims to provide guidance by outlining some major concerns that shape the ontology and the options for resolving them. The hope is that paying attention to these concerns during design will lead to a better quality architecture, and so enable more ‘intelligent’ systems. It is also hoped that understanding these concerns will lead to a better understanding of the role of ontology in M&S.
Epistemic and normative aspects of ontologies in modelling and simulation
Journal of Simulation, 2011
In modelling and simulation, ontologies can be used for the formal definition of methods and techniques (methodological ontologies), as well as for the representation of parts of reality (referential ontologies), like manufacturing or military systems, for example. Such ontologies are two sided: they are both models of a certain body of knowledge and models for automated information processing and further implementation. The first function of ontologies as pre-images (models of) has a strong epistemic nature especially for referential ontologies since they try to capture pieces of the 'semantic relations of the real world'. The second function as models for further processing, in contrast, is completely normative in nature-it is a specification of a 'formal semantics'. Unfortunately, the ideal realization of ontologies as epistemic models differs from the normative ideal. As specifications, ontologies have to be as precise (unequivocal) as possible; as representations of reality, in contrast, they have to be as descriptive as possible, which may imply ambiguity and even inconsistency in some domains. Ontology processing is particularly challenging as balancing these ideals is a domain specific task. The paper scrutinizes possibilities and fundamental limits for such a balance with a focus on simulation model interoperability and ontology-driven development based on experiences with ontologies in military projects.
2020 Winter Simulation Conference (WSC)
With diverse product mixes in fabs, high demand volatility, and numerous manufacturing steps spread across different facilities, it is impossible to analyze the combined impacts of multiple operations in semiconductor supply chains without a modeling tool like simulation. This paper explains how ontologies can be used to develop and deploy simulation applications, with interoperability and knowledge sharing at the semantic level. This paper proposes a concept to automatically build simulations using ontologies and its preliminary results. The proposed approach seeks to save time and effort expended in recreating the information for different use cases that already exists elsewhere. The use case provides first indications that with an enhancement of a so-called Digital Reference with Semantic Web Technologies, modeling and simulation of semiconductor supply chains will not only become much faster but also require less modeling efforts because of the reusability property. 1 INTRODUCTION The semiconductor industry is characterized by having complex and extensive supply chains due to its wide range of customers with varied demands for products. On the one hand, product specificity implies long and sophisticated manufacturing processes, and on the other hand, the environment comprises unpredictable demand due to the volatility of the electronics market. Furthermore, the semiconductor industry is known to be capital-intensive due to expensive equipment and the presence of rapid innovation cycles. As a result, companies in the semiconductor industry need to fiercely adapt their operations to such an evolving environment, and in turn, require their supply chains to be highly resilient and agile. In order to overcome such challenges, simulation models are often used to analyze prospective scenarios, as well as to evaluate proposed changes or new concepts. With simulation, system behavior can be better understood, and its performance can be better assessed with 'what-if' scenarios (Chien et al. 2011). However, simulation requires the acquisition, application, storage as well as maintenance of vast amounts of data. Besides, every new research requires efforts to be expended for retrieving information and recreating models that might already exist elsewhere (Benjamin et al. 2006). Furthermore, with data being generated and processed at each step of the manufacturing cycle, efficient data and knowledge management frameworks are essential. Semantic Web Technologies serve as a promising approach to integrate data from heterogeneous sources and also make it machine-readable. Ontologies, being one of the essential building blocks of the Semantic Web Technologies, provide a consistent and standardized way of information retrieval for both humans and machines (Moder et al. 2019). An ontology is an inventory of all entities existing in a domain, along with their properties and relationships.
Service-oriented simulation using web ontology
International Journal of Simulation and Process Modelling, 2012
COTS simulation packages (CSPs) have proved popular in a wider industrial setting. Reuse of simulation component models by collaborating organisations or divisions is restricted however by the same semantic issues that restrict the inter-organisation use of other software services. Semantic models, in the form of ontology, utilized by a web service based discovery and deployment architecture provides one approach to support simulation model reuse. Semantic interoperation is achieved using domain grounded simulation component ontology to identify reusable components and subsequently loaded into a CSP, modified according to the requirements of the new model, and locally or remotely executed. The work is based on a health service simulation that addresses the transportation of blood. The ontology engineering framework and discovery architecture provide a novel approach to inter-organisation simulation, uncovering domain semantics and providing a less intrusive mechanism for component reuse. The resulting web of component models and simulation execution environments present a nascent approach to simulation grids.