<mods:mods version="3.3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mods:titleInfo><mods:title>Identifying Corresponding Records Across Multiple Data Formats </mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Randy</mods:namePart><mods:namePart type="family">Kuehn</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">Calvin</mods:namePart><mods:namePart type="family">Miracle</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Managing multiple information systems that reference like items with differing data formats can be an arduous task. The complexity is compounded when attempting to make corrections to records on a large scale when data varies and unique identifiers do not exist. It is here where fuzzy logic can be an effective solution. This session will outline a multifaceted fuzzy string matching method used to analyze MARC records from Voyager and their metadata counterparts from CONTENTdm in order to correct mismatched data. </mods:abstract><mods:classification authority="lcc">Voyager</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8601">2011-05</mods:dateIssued></mods:originInfo><mods:genre>Conference or Workshop Item</mods:genre></mods:mods>