LINDA: distributed web-of-data-scale entity matching
Proceedings of the 21st ACM international conference on Information and knowledge management
Knowledge harvesting in the big-data era
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Effective string processing and matching for author disambiguation
Proceedings of the 2013 KDD Cup 2013 Workshop
Discovering implicit schemas in JSON data
ICWE'13 Proceedings of the 13th international conference on Web Engineering
Joint entity resolution on multiple datasets
The VLDB Journal — The International Journal on Very Large Data Bases
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Entity resolution (ER) is the problem of identifying which records in a database represent the same entity. Often, records of different types are involved (e.g., authors, publications, institutions, venues), and resolving records of one type can impact the resolution of other types of records. In this paper we propose a flexible, modular resolution framework where existing ER algorithms developed for a given record type can be plugged in and used in concert with other ER algorithms. Our approach also makes it possible to run ER on subsets of similar records at a time, important when the full data is too large to resolve together. We study the scheduling and coordination of the individual ER algorithms in order to resolve the full data set. We then evaluate our joint ER techniques on synthetic and real data and show the scalability of our approach.