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

TL;DR

This paper reviews recent visualization techniques for Knowledge Graphs, focusing on embedding-based methods, challenges, and future directions for improving interpretability, performance, and user interaction.

Contribution

It provides a comprehensive overview of current visualization frameworks, identifies key challenges, and distinguishes between approaches for exploration and explanation of Knowledge Graph Embeddings.

Findings

Frameworks generally satisfy UI, performance, and query support

Few frameworks meet modularity requirements

Significant differences exist between exploration and explanation approaches

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.