Empirical evaluation of Linked Data visualization tools

Desimoni, Federico; Po, Laura

doi:10.1016/j.future.2020.05.038

Cited by 22 publications

(17 citation statements)

References 18 publications

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“…e method has used tr-factorization. [8] Assessment of linked data visualization tools e study presented the analysis of the state-of-the-art tools for the visualization of linked data. List of 77 linked data visualization tools from the previous research and integrating new tools published are given.…”

Section: Resultsmentioning

confidence: 99%

Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights

et al. 2021

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Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours (KNN), decision rules (DR), decomposition tree (DT), and local transfer function classifier (LTF-C) for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem.

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Section: Resultsmentioning

confidence: 99%

Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights

et al. 2021

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“…Constant [11] extracted important insights from the crystal's geometry and physical properties for the creation of new structuring according to the methodology of knowledge-visualization. Desimoni and Po [12] presented the analysis of the state-of-the-art tools for the visualization of linked data. Full list of 77 linked data visualization tool in the previous research and integrating new tools published recently online.…”

Section: Related Workmentioning

confidence: 99%

User Knowledge, Data Modelling, and Visualization: Handling through the Fuzzy Logic‐Based Approach

Liao

Nazir

Zhou³

et al. 2021

Complexity

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In modern day technology, the level of knowledge is increasing day by day. This increase is in terms of volume, velocity, and variety. Understanding of such knowledge is a dire need of an individual to extract meaningful insight from it. With the advancement in computer and image-based technologies, visualization becomes one of the most significant platforms to extract, interpret, and communicate information. In data modelling, visualization is the process of extracting knowledge to reveal the detail data structure and process of data. The proposed study aim is to know about the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. The experimental setup is validated through the data user modelling dataset available in the UCI web repository. The results show that the model is effective and efficient in situations where uncertainty and complexity arise.

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“…Cross-domain knowledge graphs such as Wikidata and DBpedia typically convey world knowledge at a large scale. The objects in a knowledge graph are typically explored using graph-based, text-based, or mixed interfaces [3,6]. In case of existence of specific properties such as coordinates, more specialized tools with a spatio-temporal focus can be employed [12].…”

Section: Related Workmentioning

confidence: 99%

Visually Connecting Historical Figures Through Event Knowledge Graphs

Latif¹,

Agarwal²,

Gottschalk³

et al. 2021

Preprint

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Figure 1: VisKonnect interface visualizing the result of a query on soccer players: (A) An event timeline shows individual (uni-colored rectangles) and shared (multi-colored rectangles) life events of all historical figures in a user query. (B) An event map provides a geographic perspective of those events. (C) A relationship graph provides an overview of all shared events. (D) Clicking an event brings up a related article from Wikipedia. (E) A chat interface enables typing in a query and generates a short textual answer.

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Empirical evaluation of Linked Data visualization tools

Cited by 22 publications

References 18 publications

Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights

Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights

User Knowledge, Data Modelling, and Visualization: Handling through the Fuzzy Logic‐Based Approach

Visually Connecting Historical Figures Through Event Knowledge Graphs

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