Authors: Hector Garcia-Molina
Tags: 2004, conceptual modeling
Entity resolution is a problem that arises in many information integration scenarios: We have two or more sources containing records on the same set of real-world entities (e.g., customers). However, there are no unique identifiers that tell us what records from one source correspond to those in the other sources. Furthermore, the records representing the same entity may have differing information, e.g., one record may have the address misspelled, another record may be missing some fields. An entity resolution algorithm attempts to identify the matching records from multiple sources (i.e., those corresponding to the same real-world entity), and merges the matching records as best it can. Entity resolution algorithms typically rely on user-defined functions that (a) compare fields or records to determine if they match (are likely to represent the same real world entity), and (b) merge matching records into one, and in the process perhaps combine fields (e.g., creating a new name based on two slightly different versions of the name).Read the full paper here: https://link.springer.com/chapter/10.1007/978-3-540-30464-7_1
