An analysis of ontologies and their success factors for application to business

Feilmayr, Christina; Wöß, Wolfram

doi:10.1016/j.datak.2015.11.003

Cited by 97 publications

(48 citation statements)

References 20 publications

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“…Applying ontologies may be beneficial for several reasons [9]. The first advantage is the dual readability for humans and machines, thus reducing the knowledge translation steps between them.…”

Section: Application Of Ontologies In Simulation Modelsmentioning

confidence: 99%

Development of an ontology-driven, component based framework for the implementation of adaptiveness in a Jellyfish-type simulation model

Bohács

Rinkács

2017

AIS

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Simulation modelling has an ever increasing importance for complex systems. Manufacturing and related material flow or logistic systems are typical fields of application. Latest trends such as Cyber-physical systems and Industry 4.0 give a significant boost to simulation modelling as these require a digital model of the system. Complex manufacturing and related material flow systems are subject to frequent changes and pose a Big Data problem, which raises stronger requirements regarding self-adaptiveness. Conventional simulation models are to be adapted only via user interaction. Previous research steps have concentrated on the establishment of a novel simulation model structure, the so called "Jellyfish" model which unifies layout and process-type simulation models. Visualization of both aspects simultaneously enables interacting users to better understand the systems' operation compared to the conventional models. The current paper focuses on the adaptive capability of the new model. We have concentrated on the hardest type of adaptation, the structural adaptation. In this paper, an ontology-driven component based approach is presented and explained further through an example. Application of automated ontology-matching in simulation environment is a novel approach enabling the simulation model to adapt its structure without the necessity of manual interaction.

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Section: Application Of Ontologies In Simulation Modelsmentioning

confidence: 99%

Development of an ontology-driven, component based framework for the implementation of adaptiveness in a Jellyfish-type simulation model

Bohács

Rinkács

2017

AIS

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“…So, when artificial intelligence needed to coin a word to designate the formal descriptions of beings and relations they created, "ontology" was adopted. For a formal definition, one refers usually to [11], clarified by [12] and [13].…”

Section: B Ontologymentioning

confidence: 99%

Survey and Classification of Methods for Building a Semantic Annotation

Lebbos¹,

Hajjar²,

Bernard³

et al. 2017

ijacsa

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Abstract-Though Arabic is one of the five most spoken languages, little work has been done on building Arabic semantic resources. Currently, there is no agreed-upon method for building such a reliable Arabic semantic resource. The purpose of this paper is to present a comprehensive survey of different methods for building or enriching Arabic semantic resources; to study and analyze each method; and to categorize the methods according to their properties. This work should contribute to the definition of new methods and help researchers on Arabic semantics to fit their work in the panel of existing ones.

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“…These type of ontologies capture knowledge in a problemsolving independent way (Studer et al, 1998). The major application of ontologies is in the semantic web, which provides a platform for automatically processing data and information using extensible metadata (Feilmayr & Wöß, 2016). The Mediation Bridge Ontology (MBO) is a representation ontology used to store the correspondences between matching ontologies, enabling interoperability in the semantic web (Khan et al, 2015).…”

Section: Representation Ontologiesmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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An analysis of ontologies and their success factors for application to business

Cited by 97 publications

References 20 publications

Development of an ontology-driven, component based framework for the implementation of adaptiveness in a Jellyfish-type simulation model

Development of an ontology-driven, component based framework for the implementation of adaptiveness in a Jellyfish-type simulation model

Survey and Classification of Methods for Building a Semantic Annotation

Towards knowledge modeling and manipulation technologies: A survey

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