User Defined Mapping Match Types

Defining match types to match better and faster

Zingg Enterprise Feature

Leveraging domain expertise to push matching accuracy. Also save time massaging data before matching.

While the Zingg matching algorithms learn the variations from the user labels, completely different attributes pose a challenge to matching accuracy. In the case of nicknames, or company abberviations, domain input can greatly enhance matching accuracy. Zingg's MAPPING match type is built for such cases.

To leverage user expertise for nicknames for example, the user can supply Zingg with a mapping json file nicknames.json which denotes nicknames. Zingg has prebuilt mappings for company names, nicknames etc. Or you can define your own mappings too.

Here is the structure of the json:

[  
  ["Will", "Bill", "William"],
  ["John", "Johnny", "Jack"],
  ["Robert", "Rob", "Bob", "Bobby"],
  ["Charles", "Charlie", "Chuck"],
  ["James", "Jim", "Jimmy"],
  ["Thomas", "Tom", "Tommy"]
...
]   

Each line here represents common nicknames which represent the same name.

To use this mapping within Zingg, define the field's match type as MAPPING_<filename> which in our case would be mapping_nicknames

"fieldDefinition":[
   	{
   		"fieldName" : "name",
   		"matchType" : "mapping_nicknames, fuzzy",
   		"fields" : "name",
   		"dataType": "string"
   	}

Transform and Match

The MAPPING match type can also be used to transform and normalise categorical data. Let us say different data sources have different representations of gender. In one, gender is represented as M and F, in another it is noted as Male, Female and in the third as 1 and 2. Instead of transforming the gender column beforehand, one could create a mapping json called gender.json .

[  
  ["M", "Male", "1"],
  ["F", "Female", "2"]
]  

When we use it for the gender field like below, Zingg would automatically handle the transfomration so that you dont have to.

"fieldDefinition":[
   	{
   		"fieldName" : "gender",
   		"matchType" : "mapping_gender, exact",
   		"fields" : "gender",
   		"dataType": "string"
   	},