Tag Vector Series: Table of Contents

• Tag Vector: Part 0: Introduction
• Tag Vector: Part 1: Tag Vector Convention
• Tag Vector: Part 2: History of the Tag Vector Convention
• Tag Vector: Part 3: Future of Tag Vector System

Tag Vector is currently a convention to connect "tags" together with vector arrows, showing the contextual relationship between tags. The tags inference level in relation to each other can be inferred as being up↑ or down ↓^[1]

The tag person has a higher inference level than name as name is an attribute of person.

const person_name = 'Elon Musk'
// person & name are tags
// person & name are connected as a 1 dimensional right-arrow vector: person->name

Inferred vectors can also run left <-

Is inference, vectors can also be in the left direction:

const name_person = 'Elon Musk'
// name is inferred to be downstream of person
// The vector is name<-person

Default Direction when tags are on the same inferential level

If two tags are on the same inferential level, such as person & company, the vector arrow will be -> by default.

const person_company = { name: 'SpaceX' }
// person->company

The default direction can also be <- (left). A <- default direction should be documented. Future tooling will include the default direction for code & text analysis.

// <- default
const company_company = { name: 'SpaceX' }
// company<-person

Overriding the Default Direction

The default direction can be overridden in code with a comment above or before the statement.

// <-
const company_company = { name: 'SpaceX' }
// company<-person

Tag Vectors with 3 or more tags

A tag vector can be composed of any number of tags. The inference level rules are applied to the first & last tag. In the following case:

const company_person_name = 'Elon Musk'
// inferred as company->person->name

the company & name tags are inferred against each other. company has a higher inference level than name so the inferred direction is ->. Conversely:

const name_person_company = 'Elon Musk'
// inferred as name<-person<-company

is in the <- direction from inferring name having a lower inference level than company.

An ambiguous inference level follows the same rules above:

const person_registry_company = { name: 'SpaceX' }
// person->registry->company

or

// <- default
const company_registry_person = { name: 'SpaceX' }
// company<-registry<-person

multi-dimension tag vectors

Multiple tag vectors can also be composed into a multi-dimension tag vector.

const highest_net_worth__company_person_name = 'Elon Musk'
// highest->net->worth-->company->person->name

The multi-vector is composed to two vectors highest->net->worth & company->person->name with a -->. Note there are two underscores __ used to join the single underscore _ tag vectors.

Additional dimensions can be chained together:

const as_of_2023__highest_net_worth__company_person_name = 'Elon Musk'
// as->of->2023-->highest->net->worth-->company->person->name

The inference levels of the name vectors are compared, with the more abstract has a higher inference level than the concrete. In grammar, the predicate as->of->2023 has a higher inference level than the subject company->person->name.

Continuing with inference levels in grammar; an adjective, adverb, preposition, or conjunction as would be more abstract than a noun 2023.

Data Structure Shape

The Tag Vector Convention can encode the shape of the data structure.

Array

const company_person_name_a = [
	'Elon Musk',
	'Mark Zuckerberg',
]
// company->person->name->a(array)

Multi-dimensional Array

const company_a_person_name_aa = [
	['Elon Musk', 'Zachary Kirkhorn'],
	['Mark Zuckerberg', 'Susan Li'],
]
// company->a->person_name->aa
// A 2-dimensional array, company x person_name

Higher dimensions can also use a number to express the dimensionality.

const d1_a_d2_a2_d3_a3_d4_a4:number[][][][] =
	[ // d1
		[ // d2
			[ // d3
				[ // d4
					1
				]
			]
		]
	]

Record

const company_id_R_ceo_name:Record<string, string> = {
	'tesla': 'Elon Musk',
	'facebook': 'Mark Zuckerberg',
}

Map

const company_id_M_ceo_name:Map<string, string> = new Map([
	['tesla', 'Elon Musk'],
	['facebook', 'Mark Zuckerberg'],
])