Product configuration knowledge modeling using ontology web language

Yang, Dong; Miao, Rui; Wu, Han; Zhou, Yi

doi:10.1016/j.eswa.2008.05.026

Cited by 90 publications

(51 citation statements)

References 8 publications

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“…The research on product customization technology can be divided into three groups [12,13]. The first group is a management model based on customer demands, the second one is a management model based on product configuration, and the third one is a management mode based on customer online customization.…”

Section: Personalized Business Modesmentioning

confidence: 99%

Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM, Trust and Attitude

2018

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Abstract:With the rapid development of economics and social businesses, users' business demand has changed a lot. More and more people want to personalize their business modes so that they can get better experiences in business and learning activities. The key factor of personalized business mode is to consider users' individual needs on business activities, so that users can receive differentiated services. Users' satisfaction on personalized services will effectively improve the consuming experience of users, which is helpful for business organizations to strengthen their competitive power in business environments. However, will users wish to participate in personalized businesses? This is a crucial issue for developing personalized businesses. Aiming to solve this problem, this paper analyzes the major factors influencing user acceptance of personalized business modes. Then, we propose a research model that enhances the TAM (Technology Acceptance Model) model with trust and attitude to depict the influence from several variables to user acceptance of personalized business modes. Further, we use the structural equation method to conduct an empirical analysis on questionnaire data from the Internet. The results in terms of many kinds of data analysis show that trust and the TAM factors (perceived usefulness and perceived ease of use) have significant influence on user acceptance of personalized business modes. In addition, there are partial intermediate relationships existing among the factors of the research model.

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Section: Personalized Business Modesmentioning

confidence: 99%

Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM, Trust and Attitude

2018

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“…The rule-based adaptive inference engine is then used to detect, diagnose faults and calculate risks for each fault propagation scenario, which is in the third process. The fourth process is concerned with planning and evaluation of recovery actions based on the diagnosed faults [5,6].…”

Section: The Integrated Methodologymentioning

confidence: 99%

A Synergetic Intelligent Fault Prognosis Framework to support Product Life Cycle Considering Environmentally Conscious Production

Ebrahimipour¹

2017

Int J Swarm Intel Evol Comput

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The recent problems of increased oil prices, global warming, and environmental pollution highlighted the urgent need for cost effective, reliable and environmentally conscious production process. Hence to achieve clean and healthy production, the chemical process industry strives to continually improve their preparedness and awareness through adaptive inference logic by effectively extracting and signaturing cascade clues from past experiences and predicting the possible scenarios of risk and its sources. These sources are usually related to equipment life cycle, starting from suppliers' evaluation and ending by its salvage or disposal. Methods are thus needed to effectively utilize data collected and knowledge available in order to make the right decision at the right moment. Despite the considerable technological advancement, these decisions still depend heavily on human expertise, which is, although very valuable, are subject to errors, and may be lost due to death, retirement or resignation. Therefore, an integrated equipment health management system that takes into consideration the equipment life cycle, which leads to environmentally conscious production, is proposed. In order to manage and develop environmentally conscious plant operation, it is essential to provide a synergetic intelligent fault diagnosis and prognosis framework embedded in systematic interoperable platform with respect to product life cycle, process safety and environmental measures. The proposed system employs a systematic expert knowledge structure considering operation execution, process safety and control, warranty policies, and environmental issues during equipment life cycle to assist the user in evaluating uncertainties and the process of decision making.

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“…OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDFS) by providing additional vocabulary along with a formal semantics [15] The SWRL was used to describe the relationship between the rules. SWRL is based on semantic rules [16].…”

Section: Web Ontology Language (Owl)mentioning

confidence: 99%

Towards an Ontology for UML State Machines

Belgueliel¹,

Bourahla²,

Brik³

2014

LNSE

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Abstract-Ontology is a conceptual model that is used to represent the concepts in a domain and relationship between the concepts. It can be used for sharing and reuse of knowledge that allows humans and machines to exchange diverse information. UML state machines used to describe the behavior of a software systems. This article aims at to provide a solution for representing UML state machine model as an ontology expressed in OWL. a method proposed is a conceptualization of UML state machine and its operation, in order to check its consistency and its conformity. we chose OWL to represent formally our state machine augmented with SWRL rules to represent the dynamic aspect of the operation system and SPARQL to query our ontology.

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Product configuration knowledge modeling using ontology web language

Cited by 90 publications

References 8 publications

Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM, Trust and Attitude

Modeling and Quantifying User Acceptance of Personalized Business Modes Based on TAM, Trust and Attitude

A Synergetic Intelligent Fault Prognosis Framework to support Product Life Cycle Considering Environmentally Conscious Production

Towards an Ontology for UML State Machines

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