SEED V3: Entity-Oriented Exploratory Search in Knowledge Graphs on Tablets

Entity-oriented information access is becoming a key enabler for next-generation information retrieval and exploration systems. Previously, researchers have demonstrated that knowledge graphs allow the exploitation of semantic correlation among entities to improve information access. However, less attention is devoted to user interfaces of tablets for exploring knowledge graphs effectively and efficiently. In this paper, we design and implement a system called SEED to support entity-oriented exploratory search in knowledge graphs on tablets. It utilizes a dataset of hundreds of thousands of film-related entities extracted from DBpedia V3.9, and applies the knowledge embedding derived from a graph embedding model to rank entities and their relevant aspects, as well as explaining the correlation among entities via their links. Moreover, it supports touch-based interactions for formulating queries rapidly.