Stunning Doodle: A Tool for Joint Visualization and Analysis of Knowledge Graphs and Graph Embeddings

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Abstract

In recent years, the growing application of Knowledge Graphs to new and diverse domains has created the need to make these resources accessible and understandable by users with increasingly diverse backgrounds. Visualization techniques have been widely employed as means to facilitate the exploration and comprehension of such data sources. Moreover, the emerging use of Knowledge Graph Embeddings as input features of Machine Learning methods has given even more visibility to this kind of representation, but raising the new issue of understandability and interpretability of such embeddings. In this paper, we show how visualization techniques can be used to jointly explore and interpret both Knowledge Graphs and Graph Embeddings. We present Stunning Doodle, a tool that enriches the classical visualization of Knowledge Graphs with additional information meant to enable the visual analysis and comprehension of Graph Embeddings. The idea is to help the user figure out the logical connection between (1) the information captured by the Graph Embeddings and (2) the structure and semantics of the Knowledge Graph from which they are generated. We detail the use of Stunning Doodle in a real-world scenario and we show how it has been helpful to interpret different Graph Embeddings and to choose the most suitable with respect to a specific final goal.

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APA

Ettorre, A., Bobasheva, A., Michel, F., & Faron, C. (2022). Stunning Doodle: A Tool for Joint Visualization and Analysis of Knowledge Graphs and Graph Embeddings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13261 LNCS, pp. 370–386). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-06981-9_22

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