Graph Summarization for Entity Relatedness Visualization

Yukai Miao, Jianbin Qin, Wei Wang

Abstract: In modern search engines, Knowledge Graphs have become a key component for knowledge discovery. When a user searches for an entity, the existing systems usually provide a list of related entities, but they do not necessarily give explanations of how they are related. However, with the help of knowledge graphs, we can generate relatedness graphs between any pair of existing entities. Existing methods of this problem are either graph-based or list-based, but they all have some limitations when dealing with large complex relatedness graphs of two related entity. In this work, we investigate how to summarize the relatedness graphs and how to use the summarized graphs to assistant the users to retrieve target information. We also implemented our approach in an online query system and performed experiments and evaluations on it. The results show that our method produces much better result than previous work.