Visualization of Knowledge Graphs with Embeddings: an Essay on Recent Trends and Methods

TL;DR

The paper surveys the visualization landscape for Knowledge Graphs (KGs) and Knowledge Graph Embeddings (KGEs), distinguishing exploratory vs. explanatory uses of embeddings. It identifies four core challenges for visualization—modularity, intuitive UI, performance on large graphs, and query support—and finds that while many frameworks deliver usable interfaces and good performance, modularity is rarely achieved and relation visualization is underexplored, especially for KGEs. The survey maps existing KG visualization tools and embedding-focused visualizations, highlighting gaps and proposing directions such as modular architectures and integration of relation-aware and explainable visualization. Overall, the work provides a structured view of the state-of-the-art and actionable guidance for developing more extensible, interpretable, and scalable KG visualization solutions with embedding-based support.

Abstract

In this essay we discuss the recent trends in visual analysis and exploration of Knowledge Graphs, particularly in conjunction with Knowledge Graph Embedding techniques. We present an overview of the current state of visualization techniques and frameworks for KGs, in relation to four identified challenges. The challenges in visualizing Knowledge Graphs include the need for intuitive and modular interfaces, performance in handling big data, and difficulties for users in understanding and using query languages. We find frameworks that generally satisfy the intuitive UI, performance, and query support requirements, but few satisfying the modularity requirement. In the context of Knowledge Graph Embeddings, we divide the approaches that use embeddings to facilitate exploration of Knowledge Graphs from those that aim at the explanation of the embeddings themselves. We find significant differences between the two perspectives. Finally, we highlight some possible directions for future work, including diffusion of the unmet requirements, implementation of new visual features, and experimentation with relation visualization as a peculiar element of Knowledge Graphs.

Figures (6)

• Figure 1: Schema of graph embedding.
• Figure 2: Number of papers grouped by year of publication
• Figure 3: Example of entities search from StarDog ExplorerStarDog
• Figure 4: Example of graph visualization from StarDog Explorer StarDog
• Figure 5: Examples of recommended chart for a KG - VizKG VizKG