Knowledge Graph Visualization: Challenges, Framework, and Implementation

Nararatwong, Rungsiman; Kertkeidkachorn, Natthawut; Ichise, Ryutaro

doi:10.1109/aike48582.2020.00034

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…Regarding frameworks, various proposals have emerged focused on different phases of knowledge discovery in graphs, for example, business understanding [16], data modeling [17], graph mining [18][19][20], graph visualization [21,22], and evaluation [23,24]. Some proposals are focused on how to model realistic graphs that match the patterns found in real-world graphs.…”

Section: Framework and Methodologiesmentioning

confidence: 99%

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A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Ortega-Guzmán,

Gutiérrez-Preciado,

Cervantes

et al. 2024

Applied Sciences

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Graph mining has emerged as a significant field of research with applications spanning multiple domains, including marketing, corruption analysis, business, and politics. The exploration of knowledge within graphs has garnered considerable attention due to the exponential growth of graph-modeled data and its potential in applications where data relationships are a crucial component, and potentially being even more important than the data themselves. However, the increasing use of graphs for data storing and modeling presents unique challenges that have prompted advancements in graph mining algorithms, data modeling and storage, query languages for graph databases, and data visualization techniques. Despite there being various methodologies for data analysis, they predominantly focus on structured data and may not be optimally suited for highly connected data. Accordingly, this work introduces a novel methodology specifically tailored for knowledge discovery in labeled and heterogeneous graphs (KDG), and it presents three case studies demonstrating its successful application in addressing various challenges across different application domains.

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Section: Framework and Methodologiesmentioning

confidence: 99%

“…In the last decade, various proposals have emerged that focus on different phases of knowledge discovery in graph databases. These proposals encompass areas such as business understanding [16], data modeling [17], graph mining [18][19][20], graph visualization [21,22], and evaluation [23,24].…”

Section: Tools and Algorithms For Graph Analysismentioning

confidence: 99%

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Ortega-Guzmán,

Gutiérrez-Preciado,

Cervantes

et al. 2024

Applied Sciences

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“…Examples of these domains include social sciences, software engineering, biology, finance, and computer networks (see, e.g., [5], [6], [7], [8], [9]). The importance of graph visualization has also been highlighted in the contexts of machine learning, knowledge discovery, and explainable AI, which further characterizes the interdisciplinary nature of this research area (see, e.g., [10], [11], [12], [13]).…”

mentioning

confidence: 99%

“…We leave it open to establish whether each of the following complete bipartite graphs is 1 + -real face or not: (i) K 3,b for b ∈ [11,18]; (ii) K 4,b for b ∈ [8,12], (iii) K 5,b for b ∈ [6,8], and (iv) K 6,6 .…”

mentioning

confidence: 99%

Graphs Drawn With Some Vertices per Face: Density and Relationships

Binucci,

Di Battista,

Didimo

et al. 2024

IEEE Access

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In addition to the content of [1], the present article: (i) contains full proofs and technical details that were omitted or only sketched in [1]; (ii) it contains a related work section; (iii) it contains a new section with results on complete graphs and complete bipartite graphs; (iv) it improves and extends the introduction and the conclusions to better motivate the subject of the paper and to highlight future research directions that can be of interest for the scientific community; (v) it extends the presentation of the results about inclusion relationships; (vi) it contains additional and revised figures, and more than 30 references in the bibliography.

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“…Data visualization is a way to understand complex data and its connections, and it helps to navigate domain models and explore knowledge graphs [135]. Moreover, data visualization can be harnessed to communicate findings to wider audiences.…”

Section: Data Visualizationmentioning

confidence: 99%

A Gut Feeling: Biomedical Knowledge Graphs for Interrelating the Gut Microbiome and Mental Health

Liu

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The thesis delves into an investigation of the association between gut microbiota and mental health by constructing biomedical knowledge graphs sourced from scientific literature, including text and depicted molecular pathway graphs, and public databases, where the information is retrieved using a combination of automated and manual techniques. We provide an extensive introductory overview of the general architecture of knowledge graph construction and a ten-rule guide for applying Semantic Web technologies effectively when constructing knowledge graphs. The thesis introduces three relevant biomedical knowledge graphs: Microbiota Knowledge Graph (MiKG), Pre-/Probiotics Knowledge Graph (PPKG) and Ketamine Pathway (KetPath) Knowledge Graph. These graphs are built upon the tripartite relationship between gut microbiota, neurotransmitters, and mental disorders. Querying the integrated knowledge graphs revealed novel insights into the intricate interplay among these elements, ranging from the general level, such as potential new overall strategies for using probiotics in treating mental disorders, to the specific level, such as particular expression patterns of the bdnf gene in certain health conditions.

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Knowledge Graph Visualization: Challenges, Framework, and Implementation

Cited by 13 publications

References 14 publications

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Graphs Drawn With Some Vertices per Face: Density and Relationships

A Gut Feeling: Biomedical Knowledge Graphs for Interrelating the Gut Microbiome and Mental Health

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