People, who encounter knowledge graphs and graph databases for the first time, inevitably think of visualizations of networks. However, this is a limited view of what knowledge graphs can provide. A knowledge graph is a model of a domain created by subject-matter experts and intelligent machine learning algorithms. Undoubtedly, visualizations can support this process, especially in the first phase of ontology creation, which is often characterized by collaborative and communicative processes. A holistic view of the knowledge graph topic will reveal that knowledge graphs or semantic layers are much more than representations of circles and arrows.
Undoubtedly, knowledge graphs provide a structure and common interface for data and enable the creation of smart multilateral relations throughout databases. Structured as an additional virtual data layer, it can link both structured and unstructured data together and at scale. In this scenario, successful knowledge graph visualizations can help answer questions like, “How are things related?” directly and address specific issues or learning situations.
However, a closer look will reveal that knowledge graphs are much more than a tool for visualizing data. Undeniably, visualization supports the analysis of data. Concurrently, it becomes clear that knowledge graphs or the ‘semantic layer’ support broad initiatives to improve data quality and data standardization in enterprises. By enabling even small amount of test data to be enriched with semantic metadata, knowledge graphs can help machine learning based projects lower costs in the data preparation phase, itself. Knowledge graphs additionally provide excellent support for conversational artificial intelligence, e.g. realize chatbots or smart helpdesks.
In conclusion, visualizations are not the ultimate purpose of a knowledge graph. They can be leveraged to meet fundamental data management challenges, such as the integration and linking of unstructured and structured databases, and enable conversational AI.
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