Look beyond the Surface: A Demo for Explaining Knowledge Graph Embeddings and Entity Similarity

Knowledge Graph embedding (KGE) methods are concerned with mapping entities and relations in a KG into a low-dimensional vector space. KGEs have been effectively used for a variety of tasks such as link prediction, and entity classification or entity similarity. However, these methods are often considered as black boxes, providing users with no insights into the information captured by the embeddings and justifications for the computed outcome on a particular task. Recently, FeaBI, a framework for interpreting pre-computed entity embeddings relying on entity neighborhoods, has been proposed. In this paper we present a demo for this work. Our intuitive and interactive demo allows users to conveniently exploit the respective framework for computing embedding-based similarity between KG entities as well as generating and visualizing explanations for the respective similarity.