MING: Mining informative entity relationship subgraphs

Gjergji Kasneci, Shady Elbassuoni, Gerhard Weikum

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

49 Scopus citations

Abstract

Many modern applications are faced with the task of knowledge discovery in entity-relationship graphs, such as domain-specific knowledge bases or social networks. Mining an "informative" subgraph that can explain the relations between k(≥ 2) given entities of interest is a frequent knowledge discovery scenario on such graphs. We present MING, a principled method for extracting an informative subgraph for given query nodes. MING builds on a new notion of informativeness of nodes. This is used in a random-walk-with-restarts process to compute the informativeness of entire subgraphs.

Original languageEnglish
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages1653-1656
Number of pages4
DOIs
StatePublished - 2009
Externally publishedYes
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: 2 Nov 20096 Nov 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

ConferenceACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Country/TerritoryChina
CityHong Kong
Period2/11/096/11/09

Keywords

  • Entity
  • Graph
  • Informative
  • Mining
  • Random
  • Relationship
  • Walk

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