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首个DIKWP模型SCI期刊专辑征稿
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首个DIKWP模型SCI期刊专辑征稿：
https://www.mdpi.com/search?authors=yucong&journal=applsci§ion=581&special_issue=204517
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 5752
Share This Special IssueSpecial Issue Editor
Prof. Dr. Yucong Duan
School of Computer Science and Technology, Hainan University, Haikou 570228, ChinaInterests: DIKW; DIKWP; knowledge graph; semantics; AGISpecial Issues, Collections and Topics in MDPI journals
Special Issue InformationDear 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 DuanGuest Editor
Manuscript Submission Information
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KeywordsDIKW
AGI
LLM
knowledge graph
semantics
cognition
formalization
DIKWP graphs
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Editorial
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Open AccessEditorial
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 758
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)
ResearchJump to: Editorial
Open AccessArticle
Modeling and Resolving Uncertainty in DIKWP Model
by Kunguang Wu andYucong Duan
Appl. Sci. 2024, 14(11), 4776; https://doi.org/10.3390/app14114776 - 31 May 2024
Viewed by 229
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.
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
<a href="https://pub.mdpi-res.com/applsci/applsci-14-04776/article_deploy/html/images/applsci-14-04776-g001-550.jpg?1718104423" title=" <strong>Figure 1</strong>
Analysis and processing of Concept Space, Cognitive Space, and Semantic Space for the content resource under Purpose-Driven.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4776'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-04776/article_deploy/html/images/applsci-14-04776-g002-550.jpg?1718104425" title=" <strong>Figure 2</strong>A case of Concept Space.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4776'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-04776/article_deploy/html/images/applsci-14-04776-g003-550.jpg?1718104427" title=" <strong>Figure 3</strong>Input and output of Cognitive Space.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4776'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-04776/article_deploy/html/images/applsci-14-04776-g004-550.jpg?1718104430" title=" <strong>Figure 4</strong>A case of Semantic Space for identification of an HFT firm.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4776'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;">Open AccessArticle
by Yingtian Mei andYucong Duan
Appl. Sci. 2024, 14(10), 3994; https://doi.org/10.3390/app14103994 - 8 May 2024
Viewed by 611
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.
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
<a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g001-550.jpg?1715269198" title=" <strong>Figure 1</strong>
Medical diagnostic differences. (<b>a</b>) Patient perceived portrait of doctor vs. doctor’s own perceived portrait. (<b>b</b>) Patient expectation of dcotor perceived portrait of symptoms vs. dcotor perceived portrait of patient Disease.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g002-550.jpg?1715269198" title=" <strong>Figure 2</strong>Data type resource.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g003-550.jpg?1715269199" title=" <strong>Figure 3</strong>Information type resource.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g004-550.jpg?1715269200" title=" <strong>Figure 4</strong>Purpose type resource.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g005-550.jpg?1715269201" title=" <strong>Figure 5</strong>DIKWP differential spatial labeling and identification schematic under uncertainty—Text A and B.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g006-550.jpg?1715269202" title=" <strong>Figure 6</strong>DIKWP uncertainty variance processing framework.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g007-550.jpg?1715269203" title=" <strong>Figure 7</strong>Processing of multi-patient same-doctor diagnostic discrepancies.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g008-550.jpg?1715269204" title=" <strong>Figure 8</strong>Processing of multi-doctor same-patient diagnostic discrepancies.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g009-550.jpg?1715269205" title=" <strong>Figure 9</strong>Case 1-Result deposited in NRB.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g010-550.jpg?1715269206" title=" <strong>Figure 10</strong>Case 1- Search for the NRB and solve for Target.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g011-550.jpg?1715269208" title=" <strong>Figure 11</strong>Schematic of spatial marking and treatment of different doctor differences.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g012-550.jpg?1715269209" title=" <strong>Figure 12</strong>Comparison of text coverage rates by the five methods.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-14-03994/article_deploy/html/images/applsci-14-03994-g013-550.jpg?1715269210" title=" <strong>Figure 13</strong>Comparison of the five methods’ capabilities in processing DIKWP uncertainty issues.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/10/3994'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;">Open AccessArticle
by Xiaohui Cui,Yu Yang,Dongmei Li,Xiaolong Qu,Lei Yao,Sisi Luo andChao Song
Appl. Sci. 2023, 13(24), 13296; https://doi.org/10.3390/app132413296 - 15 Dec 2023
Viewed by 909
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.
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
<a href="https://pub.mdpi-res.com/applsci/applsci-13-13296/article_deploy/html/images/applsci-13-13296-g001-550.jpg?1702969016" title=" <strong>Figure 1</strong>
An example of NER process.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/24/13296'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-13296/article_deploy/html/images/applsci-13-13296-g002-550.jpg?1702969018" title=" <strong>Figure 2</strong>The framework of SLRBC.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/24/13296'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-13296/article_deploy/html/images/applsci-13-13296-g003-550.jpg?1702969019" title=" <strong>Figure 3</strong>An example of SoftLexicon method.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/24/13296'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-13296/article_deploy/html/images/applsci-13-13296-g004-550.jpg?1702969020" title=" <strong>Figure 4</strong>LSTM cell structure.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/24/13296'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-13296/article_deploy/html/images/applsci-13-13296-g005-550.jpg?1702969020" title=" <strong>Figure 5</strong>Examples from word frequency dictionary.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/24/13296'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-13296/article_deploy/html/images/applsci-13-13296-g006-550.jpg?1702969022" title=" <strong>Figure 6</strong>LOSS and F1 under different hidden layer dimensions on CCKS2018.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/24/13296'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-13296/article_deploy/html/images/applsci-13-13296-g007-550.jpg?1702969023" title=" <strong>Figure 7</strong>LOSS and F1 under different hidden layer dimensions on CCKS2019.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/24/13296'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;">Open AccessArticle
Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation
by Kai Cao andYucong Duan
Appl. Sci. 2023, 13(22), 12340; https://doi.org/10.3390/app132212340 - 15 Nov 2023
Viewed by 768
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.
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
<a href="https://pub.mdpi-res.com/applsci/applsci-13-12340/article_deploy/html/images/applsci-13-12340-g001-550.jpg?1700644063" title=" <strong>Figure 1</strong>
An illustration of the projected database.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/22/12340'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-12340/article_deploy/html/images/applsci-13-12340-g002-550.jpg?1700644066" title=" <strong>Figure 2</strong>Runtime for various <span class="html-italic">k in different dataset.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/22/12340'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-12340/article_deploy/html/images/applsci-13-12340-g003-550.jpg?1700644069" title=" <strong>Figure 3</strong>Number of candidates and threshold of top-<span class="html-italic">k HAUSPs in different datasets.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/22/12340'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-12340/article_deploy/html/images/applsci-13-12340-g004-550.jpg?1700644073" title=" <strong>Figure 4</strong>Memory usage for various <span class="html-italic">k in different dataset.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/22/12340'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-12340/article_deploy/html/images/applsci-13-12340-g005-550.jpg?1700644074" title=" <strong>Figure 5</strong>Scalability of TKAUS in Scalability_10k when<math display="inline"><semantics><mrow><mi>k</mi><mo>=</mo><mn>500</mn></mrow></semantics></math>.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/22/12340'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;">Open AccessArticle
Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
by Yitao Zhang,Ruiqing Xu,Wangping Lu,Wolfgang Mayer,Da Ning,Yucong Duan,Xi Zeng andZaiwen Feng
Appl. Sci. 2023, 13(16), 9393; https://doi.org/10.3390/app13169393 - 18 Aug 2023
Viewed by 1250
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.
(This article belongs to the Special Issue Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications)
<a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g001-550.jpg?1692364148" title=" <strong>Figure 1</strong>
Multi-modal spatio-temporal ontology and knowledge graph construction framework.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g002-550.jpg?1692364149" title=" <strong>Figure 2</strong>Core classes in the multi-modal spatio-temporal ontology.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g003-550.jpg?1692364149" title=" <strong>Figure 3</strong><span class="html-italic">Plat class and its corresponding subclasses.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g004-550.jpg?1692364150" title=" <strong>Figure 4</strong><span class="html-italic">Event class and its corresponding subclasses.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g005-550.jpg?1692364151" title=" <strong>Figure 5</strong><span class="html-italic">Equipment class and its corresponding subclasses.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g006-550.jpg?1692364153" title=" <strong>Figure 6</strong>The ultimate multi-modal spatio-temporal ontology based on ship communication.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g007-550.jpg?1692364154" title=" <strong>Figure 7</strong>Patch-based image preprocessing.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g008-550.jpg?1692364157" title=" <strong>Figure 8</strong>Multi-modal spatio-temporal knowledge graph.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;"><a href="https://pub.mdpi-res.com/applsci/applsci-13-09393/article_deploy/html/images/applsci-13-09393-g009-550.jpg?1692364159" title=" <strong>Figure 9</strong>Ship communication quality prediction based on the multi-modal spatio-temporal knowledge graph.
<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/16/9393'>Full article</strong> " style="box-sizing: border-box; color: rgb(79, 86, 113); line-height: inherit; text-decoration-line: none; max-height: 1e+06px; font-weight: 700;">https://blog.sciencenet.cn/blog-3429562-1440438.html
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