Entity matching or resolution is at the heart of many integration tasks in modern information systems. As with any core functionality, good quality of results is vital to ensure that upper-level tasks perform as desired. In this paper we introduce the FBEM algorithm and illustrate its usefulness for general-purpose use cases. We analyze its result quality with a range of experiments on heterogeneous data sources, and show that the approach provides good results for entities of different types, such as persons, organizations or publications, while posing minimal requirements to input data formats and requiring no training. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Stoermer, H., Rassadko, N., & Vaidya, N. (2010). Feature-based entity matching: The FBEM model, implementation, evaluation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6051 LNCS, pp. 180–193). https://doi.org/10.1007/978-3-642-13094-6_15
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