Graph Visualization Research Articles

Visualization and Analysis of Knowledge Graph for the Integration of Traditional Culture and Rural Tourism Industry

Abstract In order to promote the integrated development of culture and the tourism industry, this paper explores the integration of traditional culture and the rural tourism industry. Firstly, a bottom-up knowledge graph construction method is designed based on the DGCN relational extraction model, Muhead-CU-FL-BE entity extraction model and Neo4j high-performance graph database. Then, based on the domain and characteristics of traditional culture and rural tourism industry integration, the knowledge graph of rural traditional culture and tourism industry integration is constructed from three aspects: line results, ontology model and description of this paper. Finally, the performance of the knowledge graph constructed in this paper is tested on the relevant dataset, and the visualization analysis of industrial integration is carried out through the constructed knowledge graph. The results show that the overall performance of the relationship extraction algorithm in this paper is around 0.7, the entity extraction algorithm has the best performance, and the overall performance is around 0.8, and the ratio of the public ancestor nodes of 5 times linking in the 5th and 6th layers is greater than 0.65. The centrality of culture and tourism industry, cultural and tourism fusion, high-quality development, culture and tourism, and industry fusion are 0.78, 0.60, 0.58, and 0.80. The centrality of the keywords is 0.78, 0.60, 0.58, and 0.80, respectively, 1.00, and the strength of the salient values of each keyword is concentrated around 1~3.

A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports.

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians' writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge.

TreeJ: an ImageJ plugin for interactive cell lineage reconstruction from static images

BackgroundWith the emergence of deep-learning methods, tools are needed to capture and standardize image annotations made by experimentalists. In developmental biology, cell lineages are generally reconstructed from time-lapse data. However, some tissues need to be fixed to be accessible or to improve the staining. In this case, classical software do not offer the possibility of generating any lineage. Because of their rigid cell walls, plants present the advantage of keeping traces of the cell division history over successive generations in the cell patterns. To record this information despite having only a static image, dedicated tools are required.ResultsWe developed an interface to assist users in the building and editing of a lineage tree from a 3D labeled image. Each cell within the tree can be tagged. From the created tree, cells of a sub-tree or cells sharing the same tag can be extracted. The tree can be exported in a format compatible with dedicated software for advanced graph visualization and manipulation.ConclusionsThe TreeJ plugin for ImageJ/Fiji allows the user to generate and manipulate a lineage tree structure. The tree is compatible with other software to analyze the tree organization at the graphical level and at the cell pattern level. The code source is available at https://github.com/L-EL/TreeJ.

A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE

Most of the existing medical knowledge maps are incomplete and need to be completed/predicted to obtain a complete knowledge map. To solve this problem, we propose a knowledge graph embedding model (Cyclic_CKGE) based on cyclic consistency. The model first uses the “graph” constructed with the head entity and relationship to predict the tail entity, and then uses the “inverse graph” constructed with the tail entity and relationship to predict the head entity. Finally, the semantic space distance between the head entity and the original head entity should be very close, which solves the reversibility problem of the network. The Cyclic_CKGE model with a parameter of 0.46 M has the best results on FB15k-237, reaching 0.425 Hits@10. Compared with the best model R-GCN, its parameter exceeds 8 M and reaches 0.417 Hits@10. Overall, Cyclic_CKGE’s parametric efficiency is more than 17 times that of R-GCNs and more than 8 times that of DistMult. In order to better show the practical application of the model, we construct a visual medical information platform based on a medical knowledge map. The platform has three kinds of disease information retrieval methods: conditional query, path query and multi-symptom disease inference. This provides a theoretical method and a practical example for realizing knowledge graph visualization.

Dual-graph hierarchical interaction network for referring image segmentation

Referring Image Segmentation (RIS) aims to extract the object or stuff from an image according to the given natural language expression. As a representative multi-modal reasoning task, the main challenge of RIS lies in accurately understanding and aligning two types of heterogeneous data (i.e. image and text). Existing methods typically conduct this task via inexplicit cross-modal fusion toward the visual and linguistic features, which are separately extracted from different encoders and hard to capture accurate image–text alignment information due to their distinct latent representation structures. In this paper, we propose a Dual-Graph Hierarchical Interaction Network (DGHIN) to facilitate the explicit and comprehensive alignment between the image and text data. Firstly, two graphs are separately built for the initial visual and linguistic features extracted with different pre-trained encoders. By means of graph reasoning, we obtain a unified representation structure for different modalities to capture the intra-modal entities and their contexts, where each projected node incorporates the long-range dependencies into the latent representation. Then, the Hierarchical Interaction Module (HIM) is applied to the visual and linguistic graphs to extract comprehensive inter-modal correlations from the entity level and graph level, which not only capture the corresponding keywords and visual patches but also draws the whole sentence closer to the image region with the consistent context in the latent space. Extensive experiments on RefCOCO, RefCOCO+, G-Ref, and ReferIt demonstrate that the proposed DGHIN outperforms many state-of-the-art methods. Code is available at https://github.com/ZhaofengSHI/referring-DGHIN.

Analyzing the decision-making process of international trade agreements using Petri nets

Abstract This paper aims to analyze regulatory cooperation in international trade agreements by combining petri net models with intuitionistic fuzzy petri net models. A security information system for international trade agreements combines formal mathematical methods with visual graphics. The correlation matrix and invariants method is utilized to analyze invariants in international trade agreements. The correlation matrix reorganization algorithm is employed to examine correlation factors in the decision-making process of international trade agreements. The results show that the fuzzy Petri net model has the least fluctuation in analyzing the information security posture value of international trade agreements, and the security posture value floats between 6.7 and 7.3. The newest ITA regulation is 0.2 higher than the traditional regulation in consistency, the embodiment of the newest ITA regulation in democracy is 0.75, and the transparency in the newest ITA regulation is increased by 0.3. This paper’s research enhances the useful data and information for analyzing the factors influencing ITAs’ decision-making.

Utilizing Knowledge Graph to Build a Scientific and Technological Knowledge Management System for Grid Industry

Abstract Based on knowledge mapping, this paper designs the application architecture of the scientific and technological knowledge management system for the power grid industry and proposes the design of key modules such as knowledge extraction, knowledge fusion and cross-checking. In terms of knowledge fusion, this paper proposes the functional design of conflict detection, entity alignment based on deep learning, data fusion based on linking and cross-validation based on confidence assessment. In terms of cross-truth, this paper investigates the credibility calculation of candidate message sets, the representation of candidate message sets, and the construction of logic rules for cross-truth models. Finally, this paper evaluates the system’s term extraction, concept recognition, and performance. The results show that the average elapsed time for each function of graph visualization, keyword search, keyword search, advanced search, and advanced search in the knowledge management system to be executed 20 times is 1.33s, 1.27s, 3.14s, 1.47s, and 3.26s, respectively, and the average response time is only 2.1s. Therefore, the knowledge management system for grid industry science and technology presented in this paper is effective.

Automated feedback for participants of hands-on cybersecurity training

Computer-supported learning technologies are essential for conducting hands-on cybersecurity training. These technologies create environments that emulate a realistic IT infrastructure for the training. Within the environment, training participants use various software tools to perform offensive or defensive actions. Usage of these tools generates data that can be employed to support learning. This paper investigates innovative methods for leveraging the trainee data to provide automated feedback about the performed actions. We proposed and implemented feedback software with four modules that are based on analyzing command-line data captured during the training. The modules feature progress graphs, conformance analysis, activity timeline, and error analysis. Then, we performed field studies with 58 trainees who completed cybersecurity training, used the feedback modules, and rated them in a survey. Quantitative evaluation of responses from 45 trainees showed that the feedback is valuable and supports the training process, even though some features are not fine-tuned yet. The graph visualizations were perceived as the most understandable and useful. Qualitative evaluation of trainees’ comments revealed specific aspects of feedback that can be improved. We publish the software as an open-source component of the KYPO Cyber Range Platform. Moreover, the principles of the automated feedback generalize to different learning contexts, such as operating systems, networking, databases, and other areas of computing. Our results contribute to applied research, the development of learning technologies, and the current teaching practice.

Construction of a platform for displaying dialectalized graphic design based on virtual reality technology

Abstract This paper uses a semantic analysis of dialects, and a dialect word vector model is constructed. Based on the dialect word vector, the visual translation graphics of the language are used as the basic element to realize dialectalized graphic design. Secondly, the feature point matching is combined with the SIFT algorithm, where the key point is to elaborate the projection transformation matrix by the polar geometry and solve the projection transformation matrix. Then, the Jacobi determinant determines the image distortion to determine the contrast correction of the two graphics. A dialectalized graphical display platform uses virtual reality technology to achieve human-computer interaction. After testing the platform, it was obtained that the error rate generated by the display method was between 0.9 and 1.3%, the display accuracy was about 95.8%, the running time required for the display method was between 2.8s and 3.9s, and the platform display performance was good.

ANALISIS JARINGAN SOSIAL PENGGUNA TWITTER DALAM SKEMA PENYEDIAAN LAPTOP BAGI TOKO KOMPUTER

Twitter is a social media that provides information for its users. Interactions on Twitter may increase the amount of data from its users. Business development cannot be separated from product competition between business actors. One way for companies to win the competition is to know the purchasing power of customers. A product's selling power can be known one way or another through user interaction on Twitter. User-generated content (UGC) results from data on Twitter. UGC data can be used to determine the product supply scheme taken by laptop products in this study. The method used to analyze the interaction is Social Network Analysis (SNA) by comparing the properties of the network. Social network modeling on UGC data was carried out on three laptops: Lenovo v14, Asus ZenBook S, and Acer Swift 3. From the graph visualization, it was found that Acer excels in two properties, namely "Size" and "Average Degree." Meanwhile, the Asus ZenBook S dominates the "Modularity" property.

A study on the relationship between visual communication and graphics in a new media-oriented logistic regression model

Abstract In this paper, we use a logistic regression model to determine the factors of regional visual communication susceptibility evaluation with new media orientation and solve the joint confidence band level independently. The verdict results of the graphical-textual feature space define the relevant symbols of graphical-textual relationships. The confidence estimation parameters of the candidate graphic texts are determined, and the textual characteristics of graphic heterogeneity are mapped to the image unified feature space. The results show that the graphical text symbolization sequences of the design model in this paper are mainly distributed on the 0-2m·s−2 point interval, and the accuracy rate of the news expression effect reaches 82.3%. It indicates that the design model in this paper can design a new type of graphical-textual relationship and fully exploit the functions of pictures and texts.

Automated multimodal sensemaking: Ontology-based integration of linguistic frames and visual data

Frame evocation from visual data is an essential process for multimodal sensemaking, due to the multimodal abstraction provided by frame semantics. However, there is a scarcity of data-driven approaches and tools to automate it. We propose a novel approach for explainable automated multimodal sensemaking by linking linguistic frames to their physical visual occurrences, using ontology-based knowledge engineering techniques. We pair the evocation of linguistic frames from text to visual data as “framal visual manifestations”. We present a deep ontological analysis of the implicit data model of the Visual Genome image dataset, and its formalization in the novel Visual Sense Ontology (VSO). To enhance the multimodal data from this dataset, we introduce a framal knowledge expansion pipeline that extracts and connects linguistic frames – including values and emotions – to images, using multiple linguistic resources for disambiguation. It then introduces the Visual Sense Knowledge Graph (VSKG), a novel resource. VSKG is a queryable knowledge graph that enhances the accessibility and comprehensibility of Visual Genome’s multimodal data, based on SPARQL queries. VSKG includes frame visual evocation data, enabling more advanced forms of explicit reasoning, analysis and sensemaking. Our work represents a significant advancement in the automation of frame evocation and multimodal sense-making, performed in a fully interpretable and transparent way, with potential applications in various fields, including the fields of knowledge representation, computer vision, and natural language processing.

Implementasi Linier Programming Modelling untuk Maksimasi Profit Penjualan Produk Seblak pada UD. XYZ di Aceh Barat

Seblak is a food that has seen an increase in demand in recent years, and is a promising culinary business that can be run with a small capital. UD. XYZ is a seblak business that started selling seblak in early 2022 in West Aceh and wants to maximize the profits from its seblak sales. One method that can do this is Linear Programming, which can determine decision-making that can group limited materials or resources to achieve an optimal or best solution. From UD. XYZ's weekly sales data, it can be transformed into a mathematical model that helps to find the maximum function of the profit from seblak sales. The linear programming calculation process in this study uses the Lingo 18.0 software to solve linear programming problems, which will be continued with the linear graph visualization of the objective function with the MATLAB software. The results of the study show that the maximum profit that will be obtained by UD. XYZ in one week is Rp. 420,000/week, this profit is already categorized as net profit that can be used for the development of UD. XYZ's seblak business. Of the three variables X1 (seblak noodles and crackers), X2 (seblak eggs), and X3 (seblak chicken/feet), variables X1 and X2 have the potential to increase maximum profit if increased. In this study, the objective point is also on the point axis (X1=0, X2=0, and X3 = 175).

Trends in photodynamic therapy for dermatology in recent 20 years: A scientometric review based on CiteSpace.

Analyze the research state and development trend of photodynamic therapy for dermatology using visual knowledge graphs derived from the Web of Science Core Collection database. The Web of Science Core Collection database was utilized as the search data source for the bibliometric analysis, and the associated articles published between January 1, 2000, and December 31, 2022, were obtained using the search terms "photodynamic therapy" and "dermatology". CiteSpace, VOSviewer, and additional tools were utilized for bibliometric analysis, and visual knowledge graphs were created. Eight hundred and thirty two articles were retrieved in total, and 747 were included following de-duplication and transformation. The country with the greatest number of publications is the United States; the primary research institution was University of Copenhagen; and the references with the highest centrality were primarily concerned with the selection of photosensitizers; High frequency keywords primarily comprised 5 aminolevulinic acid and basal cell carcinoma; and the clustering graph revealed that all keywords fell into 11 categories. In numerous areas of dermatology, photodynamic treatment is commonly employed. Current research focuses on nonneoplastic skin diseases and the choice of photosensitizers. Nonetheless, its specific mechanism and other applications merit further investigation.

DeepDense: Enabling node embedding to dense subgraph mining

Dense subgraphs convey important information and insights about a graph structure. Several applications, such as graph visualization, graph summarization, and complex network analysis rely on discovering the dense subgraphs available within the target graph. This enumeration problem has been intensively addressed by several research communities but remains a challenging problem because of its importance and use. This problem generalizes the clique problem and is thus NP-hard and not approximable in polynomial time in general graphs.In this paper, we propose to deal with this problem in a specific application case where the type of the discovered dense subgraphs is important. This is the case of graph summarization for example. However, existing dense subgraph discovery algorithms are either dedicated to specific kind of structures such as cliques and bicliques or are too general and list the dense subgraphs without giving their types. To deal with this issue, we provide a unified approach that lists dense subgraphs and provides their types. Our approach relies on deep learning. More precisely, we enrich exiting structural node embedding with extra information, computed on node neighborhoods, to effectively capture their belonging to dense subgraphs. We evaluate our approach on several datasets to attest its efficiency and its effectiveness on two main applications: graph summarization and graph clustering. Our results show that our method significantly improves the compression rate in graph summarization, achieving up to 30% size reduction for the largest graphs tested and exceeding 80% for sparse graphs. Additionally, even for highly dense graphs, our method consistently achieves a non-zero AMI score, outperforming other approaches on the clustering application.

Reconstruction of random geometric graphs: Breaking the [formula omitted] distortion barrier

Embedding graphs in a geographical or latent space, i.e. inferring locations for vertices in Euclidean space or on a smooth manifold or submanifold, is a common task in network analysis, statistical inference, and graph visualization. We consider the classic model of random geometric graphs where n points are scattered uniformly in a square of area n, and two points have an edge between them if and only if their Euclidean distance is less than r. The reconstruction problem then consists of inferring the vertex positions, up to the symmetries of the square, given only the adjacency matrix of the resulting graph. We give an algorithm that, if r=nα for any 0<α<1/2, with high probability reconstructs the vertex positions with a maximum error of O(nβ) where β=1/2−(4/3)α, until α≥3/8 where β=0 and the error becomes O(logn). This improves over earlier results, which were unable to reconstruct with error less than r. Our method estimates Euclidean distances using a hybrid of graph distances and short-range estimates based on the number of common neighbors. We extend our results to the surface of the sphere in R3 and to hypercubes in any constant fixed dimension.

THE FUTURE OF PACKAGING: MINIMALISM IN MODERN ERA

This article delves into the potential of minimalistic packaging designs as a future trend. Minimalism, a design philosophy valuing simplicity and the removal of unnecessary elements for a clean look, is applied to packaging. This involves using basic visual elements like color, typography, and graphics to create functional and uncluttered packaging. Such packaging employs limited colors, simple typography, and negative space to achieve an elegant and sophisticated appearance, gaining popularity recently. The study examines how minimalistic packaging contributes to sustainability by reducing waste and resource consumption. Moreover, it contends that such packaging imparts a sense of high functionality to products, ultimately enhancing their perceived value. Keywords: Packaging design, Minimalism, Sustainability, Functionality and Consumer perception.

Target Netgrams: An Annulus-Constrained Stress Model for Radial Graph Visualization.

We present Target Netgrams as a visualization technique for radial layouts of graphs. Inspired by manually created target sociograms, we propose an annulus-constrained stress model that aims to position nodes onto the annuli between adjacent circles for indicating their radial hierarchy, while maintaining the network structure (clusters and neighborhoods) and improving readability as much as possible. This is achieved by having more space on the annuli than traditional layout techniques. By adapting stress majorization to this model, the layout is computed as a constrained least square optimization problem. Additional constraints (e.g., parent-child preservation, attribute-based clusters and structure-aware radii) are provided for exploring nodes, edges, and levels of interest. We demonstrate the effectiveness of our method through a comprehensive evaluation, a user study, and a case study.

First Recorded Large-Scale Adoption of Virtual Reality as a Part of Curriculum

Virtual Reality (VR) has been gaining traction as a medium of training for medical students, with various studies finding a multitude of advantages. These developments have led to its inculcation into educational institutions and programs. This study intended to understand the impact of using VR training on Indian medical students. Over 200 students from KIMS were enrolled in a VR-integrated curriculum program consisting of 10 modules across an academic year (Aug 2021-June 2022). After completion of all modules in the guided and evaluation modes, each student was asked to fill out an online questionnaire. An objective analysis of the results suggested that it was an interesting form of learning (61% of students), one that was more effective than manikin training (47%), bringing an increased level of confidence post training (92%). Feedback on the modules included satisfactory learning points, room for improvement in the visual graphics, and positive opinions on the assessment aspect. Overall, this study found that medical students perceived VR training as a fun and effective way to not only educate but assess them on procedural skills. Limitations included the need for repeated practice and the need to develop more realism in the digital environment to bring greater immersion. Keywords: Virtual Reality, Healthcare Training, VR Skill Training, Medical Simulation, KIMS.

Analysis of the Current Situation and Trends of Medical Translation Research in China

Medical translation is an interdisciplinary research direction between medicine and translation, with both theoretical and practical value. This study is based on the CNKI core paper database, utilizing the CiteSpace knowledge graph visualization tool and Excel data analysis. A systematic literature review method is used to sort out 1764 articles in Chinese medical translation related journals from 2001 to 2022, in order to clarify the research content and characteristics of medical translation. A dynamic econometric analysis was conducted on the current status and trends of medical translation research in the past two decades, and the results showed that: 1) Chinese medical translation research has shown an overall upward trend in the past two decades. 2) The research direction of influential authors in Chinese medical translation research is mostly traditional Chinese medicine translation, with research institutions mainly being universities of traditional Chinese medicine, and research achievements in Western medicine translation account for a relatively small proportion. 3) The main focus of Chinese medical translation research is the English translation of traditional Chinese medicine (TCM) terms. From the perspective of highlighted words, corpus and medical translation talent cultivation have been cutting-edge topics in recent years, and it is predicted that they will continue to grow in popularity in the future.