Ginkgo-P: General Illustrations of Knowledge Graphs for Openness as a Platform

Accessibility and openness are two of the most important factors in motivating AI and Web research. One example is as costs to train and deploy large Knowledge Graph (KG) systems increases, valuable auxiliary features such as visualization, explainability, and automation are often overlooked, diminishing impact and popularity. Furthermore, current KG research has undergone a vicissitude to become convoluted and abstract, dissuading collaboration. To this end, we present Ginkgo-P, a platform to automatically illustrate any KG algorithm with nothing but a script and a data file. Additionally, Ginkgo-P elucidates modern KG research on the UMLS dataset with interactive demonstrations on four categories: KG Node Recommendation, KG Completion, KG Question Answering, and KG Reinforcement Learning. These categories and their many applications are increasingly ubiquitous yet lack both introductory and advanced resources to accelerate interest and contributions: with just a few clicks, our demonstration addresses this by providing an open platform for users to integrate individual KG algorithms. The source code for Ginkgo-P is available: we hope that it will propel future KG systems to become more accessible as an open source project.