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Applied Sciences
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Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications
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Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 12140

Special Issue Editor

Prof. Dr. Yucong Duan

E-Mail Website
Guest Editor
School of Computer Science and Technology, Hainan University, Haikou 570228, China
Interests: DIKW; DIKWP; knowledge graph; semantics; AGI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Purpose refers to the reason or intention behind something, or the motivation or aim that drives a person or organization towards a particular goal or objective. Purpose is the sense of direction and meaning that gives significance to a person's actions and decisions. DIKWP stands for Purpose-driven Data–Information–Knowledge–Wisdom, and it is an extension of the original DIKW model that emphasizes the importance of purpose and context in the process of converting data into useful knowledge and wisdom. Data refer to any set of values or facts that can be recorded, stored and used for analysis, processing or communication. Information is a collection of data or knowledge that are organized and communicated in a meaningful way. Knowledge is the understanding and awareness of information, concepts, ideas or skills acquired through learning, experience or education. Wisdom is the ability to use knowledge, experience and good judgment to make sound decisions and judgments. The DIKW+Purpose framework recognizes that knowledge creation and management is not just about collecting and analyzing data, but also about defining and achieving specific purposes or objectives. You can find some review papers that cover this subject.

By comparing Large Language Model (LLM) practices of Artificial General Intelligence (AGI) with the DIKWP model, we found that current data-centered LLMs have limitations in interacting with data, information, knowledge, wisdom, purpose and their transformations. Data-centered AGI models are incapable of answering non-statistical and individualized interactions since they have no model of the subjective purpose in the uncertainty situation, originating in incomplete, inaccurate and inconsistent DIKWP semantics. DIKWP graphs have potential in dealing with the in-capabilities of data-centered AGI models with data graphs, which are a visual representation of data that display the relationship between different variables or data points. This is a way of presenting information in a more easily understandable and intuitive format, making it useful for analysis and decision making. Information graphs, also known as ontology, are a type of graph that represent a structured and formalized representation of a particular domain of knowledge. Knowledge graphs, which are a type of graph data structure, represent knowledge as a collection of entities, their properties and the relationships between them. Wisdom graphs are a type of knowledge graph which aims to represent and organize human knowledge and insights in a structured and interconnected way. Purpose graphs are a type of graph data structure that is designed to capture and represent the relationships between an organization's goals, strategies, activities and outcomes. Thereafter, we see DIKWP graphs as a necessary and powerful supplement for the future DIKWP-empowered AGI model exploration. We call for papers on DIKW and DIKWP modeling and processing, especially those related to novel AGI models:

1. A small model of AGI/LLMs solutions based on DIKW or DIKWP: data and knowledge hybrid modeling and processing of natural language content, language processing models, etc.

2. Low computing workload AGI/LLMs solutions: ontology automation, knowledge graph, etc.

3. New DIKW formalization methods: various formalizations on common sense, cognition, etc.

4. Objectivation approaches of subjective or cognitive AGI/LLMs content.

5. Semantic DIKWP communication for 5G/6G, privacy persevering, etc.

6. Evaluation models and standardization of AGI/LLMs tests/experiments.

7. Explainable, trustworthy, reliable and responsible architecture on AGI/LLM governance.

Kind Regards,

Prof. Dr. Yucong Duan
Guest Editor

Manuscript Submission Information

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Keywords

  • DIKW
  • AGI
  • LLM
  • knowledge graph
  • semantics
  • cognition
  • formalization
  • DIKWP graphs

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Published Papers (6 papers)

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Editorial

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2 pages, 181 KiB  
Editorial
Bridging the Gap between Purpose-Driven Frameworks and Artificial General Intelligence
by Yucong Duan
Appl. Sci. 2023, 13(19), 10747; https://doi.org/10.3390/app131910747 - 27 Sep 2023
Cited by 3 | Viewed by 1370
Abstract
Artificial General Intelligence (AGI) has leaped forward in capabilities, offering applications that reach far beyond conventional machine learning systems [...] Full article
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)

Research

Jump to: Editorial

31 pages, 4883 KiB  
Article
Modeling and Resolving Uncertainty in DIKWP Model
by Kunguang Wu and Yucong Duan
Appl. Sci. 2024, 14(11), 4776; https://doi.org/10.3390/app14114776 - 31 May 2024
Cited by 2 | Viewed by 966
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
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33 pages, 13474 KiB  
Article
The DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Revolution: A New Horizon in Medical Dispute Resolution
by Yingtian Mei and Yucong Duan
Appl. Sci. 2024, 14(10), 3994; https://doi.org/10.3390/app14103994 - 8 May 2024
Cited by 5 | Viewed by 2382
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
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16 pages, 3652 KiB  
Article
Fusion of SoftLexicon and RoBERTa for Purpose-Driven Electronic Medical Record Named Entity Recognition
by Xiaohui Cui, Yu Yang, Dongmei Li, Xiaolong Qu, Lei Yao, Sisi Luo and Chao Song
Appl. Sci. 2023, 13(24), 13296; https://doi.org/10.3390/app132413296 - 15 Dec 2023
Cited by 4 | Viewed by 1675
Abstract
Recently, researchers have extensively explored various methods for electronic medical record named entity recognition, including character-based, word-based, and hybrid methods. Nonetheless, these methods frequently disregard the semantic context of entities within electronic medical records, leading to the creation of subpar-quality clinical knowledge bases [...] Read more.
Recently, researchers have extensively explored various methods for electronic medical record named entity recognition, including character-based, word-based, and hybrid methods. Nonetheless, these methods frequently disregard the semantic context of entities within electronic medical records, leading to the creation of subpar-quality clinical knowledge bases and obstructing the discovery of clinical knowledge. In response to these challenges, we propose a novel purpose-driven SoftLexicon-RoBERTa-BiLSTM-CRF (SLRBC) model for electronic medical records named entity recognition. SLRBC leverages the fusion of SoftLexicon and RoBERTa to incorporate the word lexicon information from electronic medical records into the character representations, enhancing the model’s semantic embedding representations. This purpose-driven approach helps achieve a more comprehensive representation and avoid common segmentation errors, consequently boosting the accuracy of entity recognition. Furthermore, we employ the classical BiLSTM-CRF framework to capture contextual information of entities more effectively. In order to assess the performance of SLRBC, a series of experiments on the public datasets of CCKS2018 and CCKS2019 were conducted. The experimental results demonstrate that SLRBC can efficiently extract entities from Chinese electronic medical records. The model attains F1 scores of 94.97% and 85.40% on CCKS2018 and CCKS2019, respectively, exhibiting outstanding performance in the extraction and utilization efficiency of clinical information. Full article
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
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27 pages, 899 KiB  
Article
Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation
by Kai Cao and Yucong Duan
Appl. Sci. 2023, 13(22), 12340; https://doi.org/10.3390/app132212340 - 15 Nov 2023
Cited by 1 | Viewed by 1702
Abstract
High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) [...] Read more.
High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance. Full article
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
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17 pages, 676 KiB  
Article
Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
by Yitao Zhang, Ruiqing Xu, Wangping Lu, Wolfgang Mayer, Da Ning, Yucong Duan, Xi Zeng and Zaiwen Feng
Appl. Sci. 2023, 13(16), 9393; https://doi.org/10.3390/app13169393 - 18 Aug 2023
Cited by 4 | Viewed by 2161
Abstract
In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status [...] Read more.
In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph. Full article
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
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