Object distinction: Distinguishing objects with identical names

Yin Xiaoxin, Han Jiawei, Philip S. Yu

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

Abstract

Different people or objects may share identical names in the real world, which causes confusion in many applications. It is a nontrivial task to distinguish those objects, especially when there is only very limited information associated with each of them. In this paper, we develop a general object distinction methodology called DISTINCT, which combines two complementary measures for relational similarity: set resemblance of neighbor tuples and random walk probability, and uses SVM to weigh different types of linkages without manually labeled training data. Experiments show that DISTINCT can accurately distinguish different objects with identical names in real databases.

Original languageEnglish (US)
Title of host publication23rd International Conference on Data Engineering, ICDE 2007
PublisherIEEE Computer Society
Pages1242-1246
Number of pages5
ISBN (Print)1424408032, 9781424408030
DOIs
StatePublished - 2007
Event23rd International Conference on Data Engineering, ICDE 2007 - Istanbul, Turkey
Duration: Apr 15 2007Apr 20 2007

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other23rd International Conference on Data Engineering, ICDE 2007
Country/TerritoryTurkey
CityIstanbul
Period4/15/074/20/07

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Fingerprint

Dive into the research topics of 'Object distinction: Distinguishing objects with identical names'. Together they form a unique fingerprint.

Cite this