Graph Summarization for Entity Relatedness Visualization

Yukai Miao, Jianbin Qin, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 listbased, 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.
Original languageEnglish
Title of host publication40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationTokyo, Japan
PublisherACM
Pages1161-1164
Number of pages4
ISBN (Electronic)978-1-4503-5022-8
DOIs
Publication statusPublished - 7 Aug 2017
Event40th International ACM Conference on Research and Development in Information Retrieval - Tokyo, Japan
Duration: 7 Aug 201711 Aug 2017
http://sigir.org/sigir2017/

Conference

Conference40th International ACM Conference on Research and Development in Information Retrieval
Abbreviated titleSIGIR 2017
CountryJapan
CityTokyo
Period7/08/1711/08/17
Internet address

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