Digital VikingzWhat Is a Source Term Vector?
A source term vector is a website's fixed vocabulary for entities, attributes, and relationships, with one name per thing and one verb per relationship. The source term vector keeps every page reading as one source instead of several.
Also covered on this page: Predicate consistency
What does a source term vector contain?
A source term vector holds a head term per entity, a label per attribute, a verb per relationship, and a list of banned variants.
The document is a spreadsheet with one row per term. Each row holds the canonical term, the aliases allowed once per page, and the variants the site never uses. Writers, editors, and developers read from the same sheet.
Digital Vikingz keeps a vector for its own site. The planning document is a topical map, never a content map. The writing document is a content brief, never an outline. The one entity a site is about is its central entity, never its main topic.
Aliases appear once, in the standfirst or the first attribute block, and never per section. A reader who searches by the alias still lands on the right page. The page then continues in the canonical term.
How does vocabulary drift break a source term vector?
Vocabulary drift is one thing named several ways across pages, and it splits one source into several weaker ones.
Drift starts small. One writer calls a service an audit, another calls it a review, and a third calls it an assessment. Each page is correct alone, and the site as a whole describes three services where it sells one.
Google's card patents show that features fire from phrase sets that grow by term overlap. A phrasing that lacks the head term has no overlap with the existing set, so it adds nothing to it. The patents cover cards, not web pages, so the lesson is an analogy: shared head terms connect phrasings.
Drift also breaks clusters. A group of pages that names its entities one way reads as a unit on one topic. The same group with drifting names reads as pages on several topics, each thinner than the whole.
What is semantic dilution, and how does a source term vector prevent it?
Semantic dilution is the central entity's signal spread across too many terms, pages, or unrelated entities, so no single one carries enough weight.
Drift concerns names, and dilution concerns scope. A page that covers its own entity plus four neighbors gives each one a fifth of its attention. Google's research on query term weighting gives each query term its own weight. A page concentrated on the heavy term states more about it.
The source term vector prevents dilution by listing what the site names and what it leaves out. Terms outside the vector need a decision before they appear: attribute, related entity, or out of scope. Out-of-scope terms stay off the page. The decision is recorded in the vector, so the next writer inherits it.
Dilution also happens across URLs. Two pages targeting one service split clicks, internal links, and anchors between them. The vector assigns one canonical page per entity, and the rest link to it.
What synonym policy does a source term vector set?
The synonym policy allows synonyms inside a page and bans them across a template.
Inside one page, a synonym adds coverage for how users phrase the query. Across a template, a synonym breaks the pattern every page in the set shares. A project management SaaS page covers tool, software, and app as phrasings of one query, and none of the three earns its own page.
Names never take synonyms. Brand, product, person, and place names appear in one spelling on every page. Abbreviations go in the alias line and nowhere else. A person keeps a middle initial on every page or drops it on every page.
Search language wins over internal language. Users search for installation cost while the business says pricing, so the label reads installation cost. The vector records the search term as canonical and the internal term as banned.
Why does a source term vector fix one anchor phrase per page?
One canonical anchor phrase per page tells search engines and readers the same thing about the target every time it is linked.
A Google patent on query augmentation describes treating repeated link text as a query the target page answers. The patent has lapsed, so the agency treats it as design logic, not a guarantee. The logic still holds: anchors describe the target, so consistent anchors describe it clearly.
The anchor names the target's entity and attribute, never the source page's topic and never a phrase like click here. The sentence around the anchor varies from page to page. The phrase itself stays fixed on every page that links.
Anchor phrases come from the head term of the target page. A page about the Semantic Content Audit takes Semantic Content Audit as its anchor, not audit service or our review. The topical map records the phrase next to the URL, so every writer links the same way.
What is predicate consistency in a source term vector?
Predicate consistency is one verb for each relationship, used on every page that states that relationship.
The predicate is the verb that links an entity to its object. A service page states what the service checks, returns, or includes. One page saying the audit checks and another saying it reviews writes the relationship two ways. The vocabulary has drifted at the verb.
Templates raise the stakes. Every page in a query template uses the same predicate in its first sentence, and the entity value is the only part that changes. Predicates also differ by object: pages about uploading a file and pages about sharing a post need different verbs.
The predicate set comes from the source context and the query network. The verbs users pair with the entity in searches are the verbs the site adopts. Each one is recorded in the vector beside the relationship it names.
- US 11,238,058 B2: Search and retrieval of structured information cards (Google)
- US 9,916,366 B1: Query augmentation (Google, expired)
- Koray Tuğberk Gübür, Search Engine Land: Query templates, expanding the scope of topical authority
Patents and filings describe systems Google has invented. None is a confirmed ranking factor.
What should you read after what is a source term vector?
What is a central entityA central entity is the one entity a website is about, defined in a single sentence that every page inherits.
Entity-attribute-valueEntity-attribute-value is the method of stating every fact as an entity, one property of that entity, and the property's.
MicrosemanticsMicrosemantics is the sentence-level layer of semantic SEO: subject choice, word order, and predicate choice inside each.
What is a content briefA content brief is the document that turns one node of the topical map into one page.
Semantic SEO ArchitectureSeven documents with a fixed structure. Every engagement produces all seven.
Semantic Content AuditSix checks. Each one has a defined failure and a defined fix.
Talk to usMessage the team on WhatsAppQuestions about what is a source term vector
Who maintains the source term vector?
How many terms does a source term vector hold?
What happens to published pages when the source term vector changes?
Does the source term vector apply to off-site mentions?
How does a source term vector work across languages?
Is a source term vector the same as a style guide?
Does a source term vector cover structured data?
Does a source term vector cover image captions and alt text?
How is vocabulary drift found on an existing site?
Should the vocabulary follow the terms competitors use?
When does a new term enter the source term vector?
See it applied to your site
The Semantic Content Audit uses every term in the knowledge hub. Fixed scope, written deliverable.