Integrating Knowledge Graphs and Visualization Dashboards for Advance Data Discovery in VESA

TL;DR

This paper presents VESA, a knowledge graph and visualization dashboard system that improves scientific data discovery by enabling multidimensional exploration and meaningful data connections, validated through user studies.

Contribution

The paper introduces VESA, a novel system integrating knowledge graphs with visualization dashboards to enhance data discovery in scientific datasets.

Findings

VESA received positive user feedback for ease of use.

The system demonstrated effective multidimensional data exploration.

VESA's low learning curve facilitates adoption among researchers.

Abstract

The increasing complexity and scale of scientific datasets demand advanced tools for efficient discovery and exploration. Traditional search systems often fall short in addressing the multidimensional nature of data and their intricate relationships, limiting their utility for researchers. This paper introduces the Knowledge Graph Based Visualization Search Application (VESA), which reshapes the process of data discovery by leveraging knowledge graph technology to establish meaningful connections and employing a visualization dashboard to enable multidimensional exploration. A software prototype is developed, showcasing our use case of connecting two Earth System Science repositories via a knowledge graph backend and visualization dashboard at the frontend. The framework's effectiveness was assessed against guidelines derived from a comprehensive literature review and further validated through an online user study. The evaluation revealed positive reception, highlighting VESA's low learning curve, ease of use, and potential to enhance data discovery workflows.