Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment

Adhnouss, Fateh Mohamed Ali; El-Asfour, Husam M. Ali; McIsaac, Kenneth; El-Feghia, Idris; Aburukba, Raafat; Wahaishi, AbdulMutalib

doi:10.5220/0011379200003335

Cited by 2 publications

(6 citation statements)

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“…Previous studies and models related to the topic of providing shared conceptualization structures for various settings include the work of [2,4,7,11]. For closed systems, Ref.…”

Section: Related Workmentioning

confidence: 99%

“…[11] proposed an extensional structure, while [4,10] proposed an intensional structure. A hybrid model has been proposed by [2] that combines elements of extensional and intensional structures and which is particularly suitable for decentralized environments.…”

Section: Related Workmentioning

confidence: 99%

“…In the literature [2,28], environments are categorized based on the level of agreement and coordination among observers, leading to the identification of three main categories:…”

Section: Types Of Environmentsmentioning

confidence: 99%

“…These systems necessitate a shared understanding of the environment and its entities, referred to as shared conceptualization. Representing shared conceptualization in such environments, however, is a challenging task, as it demands a balance between the flexibility of decentralized environments and the stability of closed environments [2].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed model builds upon the work of [2], who proposed a hybrid model for representing shared conceptualization in dynamic closed environments. The model consists of three levels: the domain level, the possible worlds level, and the extension level.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach to Representing Shared Conceptualization in Decentralized AI Systems: Integrating Epistemology, Ontology, and Epistemic Logic

Adhnouss,

El-Asfour,

McIsaac

et al. 2023

AppliedMath

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Artificial Intelligence (AI) systems are increasingly being deployed in decentralized environments where they interact with other AI systems and humans. In these environments, each participant may have different ways of expressing the same semantics, leading to challenges in communication and collaboration. To address these challenges, this paper presents a novel hybrid model for shared conceptualization in decentralized AI systems. This model integrates ontology, epistemology, and epistemic logic, providing a formal framework for representing and reasoning about shared conceptualization. It captures both the intensional and extensional components of the conceptualization structure and incorporates epistemic logic to capture knowledge and belief relationships between agents. The model’s unique contribution lies in its ability to handle different perspectives and beliefs, making it particularly suitable for decentralized environments. To demonstrate the model’s practical application and effectiveness, it is applied to a scenario in the healthcare sector. The results show that the model has the potential to improve AI system performance in a decentralized context by enabling efficient communication and collaboration among agents. This study fills a gap in the literature concerning the representation of shared conceptualization in decentralized environments and provides a foundation for future research in this area.

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“…Previous studies and models related to the topic of providing shared conceptualization structures for various settings include the work of [2,4,7,11]. For closed systems, Ref.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In the literature [2,28], environments are categorized based on the level of agreement and coordination among observers, leading to the identification of three main categories:…”

Section: Types Of Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Approach to Representing Shared Conceptualization in Decentralized AI Systems: Integrating Epistemology, Ontology, and Epistemic Logic

Adhnouss,

El-Asfour,

McIsaac

et al. 2023

AppliedMath

Self Cite

View full text Add to dashboard Cite

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A Hybrid Approach to Representing Shared Conceptualization in Decentralized AI Systems: Integrating Epistemology, Ontology, and Epistemic Logic

Adhnouss¹,

El-Asfour²,

McIsaac³

et al. 2023

Preprint

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The deployment of Artificial Intelligence (AI) systems in decentralized environments is on the rise, yet representation of shared conceptualization in such scenarios remains a challenging issue. The absence of a shared understanding can lead to suboptimal performance of AI systems and hinders the ability to comprehend the knowledge and beliefs of agents in the domain. This paper proposes a formal model for modeling conceptualization in AI systems that integrates ontology, epistemology, and epistemic logic. The model aims to address the gap in representing shared conceptualization in decentralized environments and enhance the performance of AI systems operating in such environments. The proposed model is a hybrid structure that blends extensional and intensional structures and leverages logic-based languages for modeling purposes. A case study in the healthcare sector is presented to illustrate the application of the proposed model. The study contributes to the existing literature by providing a formal model for representing shared conceptualization in decentralized environments, which can be utilized to optimize the performance of AI systems in these environments.

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Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment

Cited by 2 publications

References 0 publications

A Hybrid Approach to Representing Shared Conceptualization in Decentralized AI Systems: Integrating Epistemology, Ontology, and Epistemic Logic

A Hybrid Approach to Representing Shared Conceptualization in Decentralized AI Systems: Integrating Epistemology, Ontology, and Epistemic Logic

A Hybrid Approach to Representing Shared Conceptualization in Decentralized AI Systems: Integrating Epistemology, Ontology, and Epistemic Logic

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