Purpose Driven Biological Lawsuit Modeling and Analysis Based on DIKWP

Mei, Yingtian; Duan, Yucong; Yu, Lei; Che, Haoyang

doi:10.1007/978-3-031-24386-8_14

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…Drawing upon Zaharudin's perspective [31], we have constructed a conceptual graph in the Concept Space of HFT features. In this graph, we classify concepts based on the mapping of data, information, knowledge, wisdom, and purpose [7][8][9]. This classification includes data nodes, information nodes, knowledge nodes, wisdom nodes, and purpose nodes, forming data graphs, information graphs, knowledge graphs, wisdom graphs, and purpose graphs, respectively.…”

Section: Definitionmentioning

confidence: 99%

“…The DIKWP model provides a systematic analytical framework by distinguishing and correlating five dimensions: Data, Information, Knowledge, Wisdom, and Purpose, and processing them into corresponding Data Graphs (DGs), Information Graphs (IGs), Knowledge Graphs (KGs), Wisdom Graphs (WGs), and Purpose Graphs (PGs). Under the guidance of purpose [9], it assists financial enterprises in better understanding and managing complex regulatory requirements. Specifically, this study categorizes challenges as the "4-N" problems, Incompleteness, Inconsistency, Imprecision, and Incorrectness, which highlight the uncertainties faced by enterprises in complying with regulations.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the research and discoveries of predecessors [9], driven by purpose, we present in Figure 1 an analytical and processing framework for content(CT) resources across the three spaces mentioned from the perspective of stakeholders. Furthermore, based on the three aforementioned spaces and Figure 1, our definitions for Data, Information, Knowledge, Wisdom, and Purpose are as follows:…”

mentioning

confidence: 99%

“…Initially, leveraging the viewpoints presented in papers [7][8][9], we categorize content resources into several types of graphs, including Data Graphs (DGs), Information Graphs (IGs), Knowledge Graphs (KGs), Wisdom Graphs (WGs), and Purpose Graphs (PGs), and subsequently analyze and process them from the viewpoint of stakeholders driven by purpose and value. When stakeholders integrate concepts from content resources with individual or systemic intrinsic cognitive mechanisms, coupled with personal experiences and knowledge, unique understandings and interpretations are formed, thereby establishing a mapping from the Cognitive Space to the Concept Space.…”

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confidence: 99%

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Modeling and Resolving Uncertainty in DIKWP Model

Wu,

Duan

2024

Applied Sciences

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The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd–Frank Wall Street Reform and Consumer Protection Act (referred to as the “Dodd–Frank Act”), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of Cognitive Space, Concept Space, and Semantic Space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

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Section: Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Modeling and Resolving Uncertainty in DIKWP Model

Wu,

Duan

2024

Applied Sciences

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“…Based on the DIKWP fusion model, Hu [59] analyzed and processed healthcare and wellness by combining meteorological and depressive diseases. Mei et al [60] conducted a DIKWP semantic mapping search via intention-driven intelligent case adjudication to narrow the cognitive distance between the parties involved. Liu et al [61], oriented to the public safety domain, applied the DIKWP model to evaluate and maintain critical public facilities.…”

Section: Introductionmentioning

confidence: 99%

The DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Revolution: A New Horizon in Medical Dispute Resolution

Mei,

Duan

2024

Applied Sciences

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The doctor–patient relationship has received widespread attention as a significant global issue affecting people’s livelihoods. In clinical practice within the medical field, applying existing artificial intelligence (AI) technology presents issues such as uncontrollability, inconsistency, and lack of self-explanation capabilities, even raising concerns about ethics and morality. To address the problem of doctor–patient interaction differences arising from the doctor–patient diagnosis and treatment, we collected the textual content of doctor–patient dialogues in outpatient clinics of local first-class hospitals. We utilized case scenario analysis, starting from two specific cases: multi-patient visits with the same doctor and multi-doctor interaction differences with the same patient. By capturing the external interactions and the internal thought processes, we unify the external expressions and internal subjective cognition in doctor–patient interactions into interactions between data, information, knowledge, wisdom, and purpose (DIKWP) models. We propose a DIKWP semantic model for the doctor–patient interactions on both sides, including a DIKWP content model and a DIKWP cognitive model, to achieve transparency throughout the entire doctor–patient interaction process. We semantically–bidirectionally map the diagnostic discrepancy space to DIKWP uncertainty and utilize a purpose-driven DIKWP semantic fusion transformation technique to disambiguate the uncertainty problem. Finally, we select four traditional methods for qualitative and quantitative comparison with our proposed method. The results show that our method performs better in content and uncertainty handling. Overall, our proposed DIKWP semantic model for doctor–patient interaction processing breaks through the uncertainty limitations of natural language semantics in terms of interpretability, enhancing the transparency and interpretability of the medical process. It will help bridge the cognitive gap between doctors and patients, easing medical disputes.

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