Data visualization is a much-studied area in computer science, and knowledge graphs are an archetypal example of these innovative data display techniques. When compared to spreadsheets or relational databases, graph databases are optimized to ask questions and retrieve answers to those queries from high-volume data in an efficient manner. Primarily because in graph databases, the relationships between entities in interconnected datasets are explicitly represented. The advantage of this is it allows us to traverse these relationships in much more efficient ways.
Since data visualization tools can accelerate and make the analysis of copious amounts of information accessible, they hold pronounced explanatory power when exercised appropriately. Additionally, the act of building a knowledge graph can offer benefits well beyond the visualization it produces.
The fundamental point of visualizing datasets through a knowledge graph provides the user with direct access to new and powerful perspectives on the underlying data. For example, it can be employed for facilitating peer review for scientific authors and editors. Furthermore, a knowledge graph approach could be applied to many other types of entities, such as those in the financial or chemical domains.
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