MING: mining informative entity relationship subgraphs

Authors:
Gjergji Kasneci;Shady Elbassuoni;Gerhard Weikum
Affiliations:
Max-Planck Institute for Informatics, Saarbrücken, Germany;Max-Planck Institute for Informatics, Saarbrücken, Germany;Max-Planck Institute for Informatics, Saarbrücken, Germany
Venue:
Proceedings of the 18th ACM conference on Information and knowledge management
Year:
2009

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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.