The following provides a concise SKOS-based taxonomy used for representing the Senzing entity resolution (ER) results in the computable semantics of RDF, based on the data mappings defined in the Senzing Entity Specification.

One intention is to align with popular controlled vocabularies used by Senzing partners, such as NIEM by OASIS Open, FollowTheMoney by OpenSanctions, and the Beneficial Ownership Data Standard by Open Ownership.

Effectively, this creates as a domain-specific thesaurus, which may be used:

• to provide a "backbone" for constructing an Entity Resolved Knowledge Graph
• within information exchange workflows based on standards such as NIEM
• as a core component for developing a semantic layer
• to guide context engineering in AI applications

Namespace: https://github.com/senzing-garage/sz-semantics/wiki/ns#
Suggested prefix: sz:
Source: https://github.com/senzing-garage/sz-semantics/blob/main/domain.ttl
Updated: 2015-11-02
Authors: Paco Nathan, Jessica Talisman, Jeff Butcher

A metadata application profile (MAP) is a specification which describes how metadata standards should be used within a specific application, project, or community context. This provides a bridge between general-purpose metadata standards and the real-world practices for a given domain. Instead of inventing an entirely new metadata schema, a MAP serves to:

• select elements from existing metadata standards -- e.g., SKOS, Dublin Core, BODS, etc.
• refine the meaning of the elements used -- e.g., require that dcterms:creator must be a person, not an organization.
• constrain usage -- e.g., requiring particular fields, limiting values to controlled vocabularies, etc.
• extend with local elements as needed.

Example:

sz:96 a sz:Organization ;
   skos:prefLabel "Univrsl Export Inc"@en ;
   sz:lemma_phrase "NOUN.univrsl NOUN.export NOUN.inc";
   prov:wasDerivedFrom sz:ds_customers_2072, sz:ds_reference_2071 .

[] rdf:object sz:96 ;
   rdf:predicate skos:related ;
   rdf:subject sz:97 ;
   sz:match_key "+REL_POINTER(:GLOBAL PARENT)" ;
   sz:match_level 11 .

Example:

sz:22 a sz:Person ;
   skos:prefLabel "Daniella Shaw"@en ;
   sz:lemma_phrase "NOUN.daniella NOUN.shaw" ;
   prov:wasDerivedFrom sz:ds_customers_1032, sz:ds_customers_1033 .

[] rdf:object sz:ds_customers_1032 ;
   rdf:predicate skos:exactMatch ;
   rdf:subject sz:22 ;
   sz:match_key "INITIAL" ;
   sz:match_level 0 .

[] rdf:object sz:ds_customers_1033 ;
   rdf:predicate skos:exactMatch ;
   rdf:subject sz:22 ;
   sz:match_key "+NAME+DOB+PHONE" ;
   sz:match_level 1 .

Example:

sz:ds_customers_1032 a sz:DataRecord ;
   dc:identifier "1032" ;
   skos:prefLabel "Daniella Shaw"@en ;
   prov:wasQuotedFrom sz:ds_customers .

sz:ds_customers_1033 a sz:DataRecord ;
   dc:identifier "1033" ;
   skos:prefLabel "Shaw Daniella"@en ;
   prov:wasQuotedFrom sz:ds_customers .

sz:ds_customers a dcat:Dataset ;
   dc:identifier "customers" .