Radix Analytics Pvt Ltd

Address/Entity Matching

Radix Analytics Pvt Ltd > Public Sector & Education > Address/Entity Matching

Issues & Objectives

To develop ML based system for matching addresses and entity names:

  • Same address or entity names can be written in few different ways
  • It may have spelling issues, order might be different, abbreviations may exist
  • Our aim is to build a ML based system that can accurately match the addresses and entity with the ones already in our database

Results

Test recall (KPI) for different models are given as below:

  • Address Matching: 87.47 %
  • Entity Matching: 88.62 %

Project information

Techniques

Text Analytics

Client

Corporate Data Aggregator

Industries

Public Sector & Education

Location

India

address-matching

Challenges

  • The database is mostly on addresses and entities (company names & person names)
  • Different models are being built for address and entity separately
  • Entity matching can be tricky as it contains both person name and company name which ideally should require different set of features
  • Handling and Searching in a large amount of data
  • Annotation of data consistently by multiple SMEs was a challenge

Solution

  • Solr is used to search an address/entity from the database and to determine top 10,20 or 30 results
  • The top results are then passed to ML to determine the best match among them
  • MariaDB (SQL) database is used
  • The deployment framework is Flask API + Gunicorn

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