Relational clustering for multi-type entity resolution

Authors:
Indrajit Bhattacharya;Lise Getoor
Affiliations:
University of Maryland, College Park, MD;University of Maryland, College Park, MD
Venue:
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Year:
2005

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Abstract

In many applications, there are a variety of ways of referring to the same underlying entity. Given a collection of references to entities, we would like to determine the set of true underlying entities and map the references to these entities. The references may be to entities of different types and more than one type of entity may need to be resolved at the same time. We propose similarity measures for clustering references taking into account the different relations that are observed among the typed references. We pose typed entity resolution in relational data as a clustering problem and present experimental results on real data showing improvements over attribute-based models when relations are leveraged.