Address Normalization & Matching

<p>This use case demonstrates how <strong>M|Box’s Alias Generator, Phrase Indexer, and Approximate Phrase Index Matcher </strong>nodes can be combined to efficiently <strong>index and search address data</strong>, even in the presence of abbreviations, spelling variants, and other fuzzy input patterns.</p><p>Workflow Overview:</p><ol><li><p>The workflow loads:</p><ul><li><p>A reference table of full, normalized addresses (without abbreviations)</p></li><li><p>An alias dictionary mapping canonical terms (e.g. <em>“Street”</em>) to known alternatives (e.g. <em>“St”, “Str”</em>)</p></li><li><p>A search table containing addresses with abbreviations and other variants</p></li></ul></li><li><p>The reference addresses are indexed using the <strong>Phrase Indexer</strong>, while the <strong>Alias Generator</strong> ensures that defined aliases are treated as equivalent terms during indexing.</p></li><li><p>Incoming search addresses are matched against the index using the <strong>Approximate Phrase Index Matcher</strong>, allowing abbreviations in the input without negatively affecting match quality.</p></li></ol><p>Index-based matching with the <strong>M|Box Phrase Indexing and Alias Generation nodes</strong> ensures robust and scalable address search, even in the presence of abbreviations and spelling variants. This makes it ideal for:</p><ul><li><p>Harmonizing addresses from heterogeneous sources with inconsistent formatting</p></li><li><p>Matching user-entered or third-party address data against a standardized reference set</p></li><li><p>Improving address search and matching quality without sacrificing performance or explainability</p></li></ul><p></p>