A framework for semantic link discovery over relational data

  • Authors:

  • Oktie Hassanzadeh;Anastasios Kementsietsidis;Lipyeow Lim;Renée J. Miller;Min Wang

  • Affiliations:

  • University of Toronto, Toronto, OH, USA;IBM T.J. Watson Research Center, Yorktown Heights, OH, USA;University of Hawaii at Manoa, Honolulu, HI, USA;University of Toronto, Toronto, ON, Canada;IBM T.J. Watson Research Center, Yorktown Heights, NY, USA

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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Abstract

Discovering links between different data items in a single data source or across different data sources is a challenging problem faced by many information systems today. In particular, the recent Linking Open Data (LOD) community project has highlighted the paramount importance of establishing semantic links among web data sources. Currently, LOD sources provide billions of RDF triples, but only millions of links between data sources. Many of these data sources are published using tools that operate over relational data stored in a standard RDBMS. In this paper, we present a framework for discovery of semantic links from relational data. Our framework is based on declarative specification of linkage requirements by a user. We illustrate the use of our framework using several link discovery algorithms on a real world scenario. Our framework allows data publishers to easily find and publish high-quality links to other data sources, and therefore could significantly enhance the value of the data in the next generation of web.