Entity grounding

Entity grounding connects a brand or topic mentioned in web content to a verifiable identity in a knowledge graph. It turns a name in a sentence into a thing a system can look up. Without it, your name is a string that resembles other strings.

Generative engine optimization structures content and brand signals so language models can use them. Grounding is the step that decides whether those signals attach to you at all. Everything else in the discipline assumes the attachment already worked.

JSON-LD schema provides machine-readable metadata that defines entities and their relationships explicitly, in the page code rather than in the prose. It states what a page is about instead of leaving a system to infer it. It is the cheapest grounding signal available.

An llms.txt file offers summary information about a site directly to language models, in one place and in plain text. It describes the site as a whole rather than page by page. This site publishes one.

Share of model measures the proportion of generated conversational answers that cite or mention a brand. It is the generative equivalent of share of voice. It is the number that tells you whether grounding is working.

A knowledge graph stores entities and the relationships between them as structured data that generative systems consult. It is where a name becomes a node with properties. Being in one is the difference between being described and being known.

Passage retrieval extracts concise segments rather than whole pages. A retrieved passage carries whatever identity it states inside itself. A paragraph that says the company without naming it arrives at the model unattached.

Citation rate tracks how often an entity or URL is explicitly referenced in generated output. It counts attribution rather than attention. Grounding raises it by making attribution possible.

The information density ratio measures verifiable facts and data per block of words. Systems that select material prefer density to volume. A page with one fact and six hundred words of framing is low density regardless of how well it reads.

Wikidata is a public knowledge repository that language models consult to confirm entities. It is open, editable and widely reused. For many companies it is the single most consequential external record they have never looked at.

A sameAs property links a local entity definition to an authoritative external record. It says this company here is that company there. It is how a claim on your own site becomes checkable against something outside it.

AI Overviews display synthesized answers at the top of results and rely on grounded sources to build them. Ungrounded material can still be used and is harder to attribute. Grounding is what turns being used into being named.

The advice says add schema, add sameAs, get into Wikidata. It treats markup as the whole job. It says nothing about resolving ambiguity, maintaining entities over time, reconciling conflicting records, or what happens when a model already holds a wrong version of you internally.

Entity disambiguation decides which of several possible entities a mention refers to, using the surrounding context. Markup on your page cannot prevent a system elsewhere from resolving your name to a different company, and for common names that happens constantly.

Entity resolution is record linkage: deciding that two records describing something are the same thing. Where your company appears in dozens of records with slightly different names and addresses, resolution is what merges them, and inconsistency is what prevents it.

Entity canonicalization establishes one standard identifier used everywhere for the same thing. Local schemas without a shared canonical identifier produce many local definitions that no system can confidently merge into one entity.

Entity salience measures how important an entity is within a text, rather than whether it was mentioned. A page can mention you once in passing and be counted as covering you, while a page that is genuinely about you scores the same.

Entity alignment matches the same entity across different knowledge graphs. Local markup does not propagate automatically, and an entity that exists in one graph and not in another is invisible to everything built on the second.

Entity coverage measures how complete an entity's attributes are, rather than how many pages mention it. An entity with a name and nothing else is present and unusable.

Mention detection is the extraction step that recognizes an entity term in running text. Names that are ordinary words, or that vary in form across a site, are frequently not detected at all.

Fine-grained entity typing assigns detailed subtypes rather than broad classes. A system that knows only that you are an organization cannot tell whether you belong in an answer about your specialism.

A canonical URI is a globally unique, persistent identifier for an entity, as distinct from the URL of a page about it. Pages move and are rewritten; an entity identifier is supposed to outlive them.

Entity consolidation is the ongoing maintenance of an entity across the web sources that describe it. Records drift apart as staff, addresses and descriptions change, and nothing consolidates them unless somebody does.

Disambiguation context is the semantic neighbourhood around a mention that helps decide which entity it is. The words near your name do more to identify you than the markup on the page.

An entity co-occurrence graph represents which entities appear together across a corpus. Who you are named alongside is a stronger signal of what you are than anything you say about yourself.

Knowledge base completion is the algorithmic prediction of relationships that are missing from a graph. Systems infer, which means an incomplete profile is filled in with guesses rather than left blank.

Relation extraction maps sentences onto predicate triples: subject, relation, object. A fact stated across two sentences, or hedged, often yields no triple at all.

Knowledge fusion resolves conflicts when sources disagree about the same entity. Where your own site, a directory and an old press release disagree, fusion decides which version wins, and it is not necessarily yours.

Ontology alignment maps between different vocabularies describing the same domain. Systems that use other vocabularies need a mapping, and without one your description does not carry across.

Parametric knowledge is what a model holds in its own weights from training, as opposed to what it retrieves. A model that learned an outdated fact about you will keep stating it even while retrieving a corrected page.

If a model knew little-known entities as well as famous ones, the three rows would match. Correct answers fall by half or more from the most popular third to the least popular.

Non-parametric knowledge is the external material a system retrieves at the moment of answering. The distinction from what the model already holds decides whether publishing a correction can help at all.

A hallucination rate measures how often generated statements are fabricated. A brand with strong visibility and an unmeasured hallucination rate is being described frequently and possibly wrongly.

Attribution accuracy evaluates whether a cited source actually supports the statement attached to it. A citation pointing at you for a claim you never made carries your name into somebody else's error.

Fact verification is automated evidence checking of individual claims. Claims that cannot be verified against evidence are dropped from careful answers, which is a selection effect nobody optimizes for.

DBpedia is a public knowledge graph derived from Wikipedia, widely used in research and in systems built outside the major search providers. Entities present in one public graph and absent from another are unevenly known.

YAGO is an academic semantic knowledge base combining Wikipedia, WordNet and GeoNames. Systems built on academic bases see a different web than the commercial ones do.

A triple store is a database built to hold subject-predicate-object statements. Understanding the storage explains why some statements survive extraction and others evaporate.

A property graph is a graph model where nodes and edges carry key-value attributes, as an alternative to RDF. Different model, different assumptions about what can be expressed, and content prepared for one is not automatically usable in the other.

Schema matching automatically aligns differing dataset structures. Where your data sits in several systems with different field names, matching is what merges them, and where it fails the data stays fragmented.

Semantic drift is the change in an entity's meaning as corpora update over time. A term that identified your category five years ago may now identify something adjacent, and your grounding drifts with it.

A knowledge graph embedding represents entities and relations as continuous vectors rather than as symbols. Systems that reason in vector space do not match your name; they match its position, which is shaped by everything published about you.

Cross-lingual entity alignment matches the same entity across languages. A company described in two languages without alignment is two partially-known entities rather than one well-known one.

In-context knowledge injection supplies entity information explicitly inside a prompt rather than relying on retrieval. Where systems accept structured payloads, the form of what you publish determines whether it can be injected cleanly.

Data provenance records the complete origin and transformation history of information. Provenance is what allows a claim to be traced back to you rather than absorbed anonymously.

RDF Schema is the foundational W3C specification for describing classes and properties of semantic data. The commercial vocabularies sit on top of it, and the parts they omit are still expressible underneath.

The Web Ontology Language is a formal standard for expressing description logic about a domain. Formal ontologies express constraints and inferences that lightweight markup cannot, which matters wherever correctness is checkable.

Epistemic provenance distinguishes the creation of an original claim from its secondary distribution. Being the origin of a claim and being one of its repeaters look identical in most measurement, and they are worth entirely different amounts.

Knowledge unlearning removes specific information from a trained model. Publishing new content does not reach what the model already holds, and the remedies for that are immature and slow.

Conceptual density measures topic nodes per unit of text rather than words. Dense material is preferred by systems that must compress, and length without density is the most common failure in published content.

Claim extraction isolates individual factual assertions from prose. A system that cannot separate one checkable claim from surrounding argument has nothing definite to attribute to you.

Prompt conditioning steers generative behaviour through the framing of the request. Since the framing shapes which sources are consulted, some visibility outcomes are decided before retrieval begins.

Reference resolution works out what indirect expressions point to: it, the company, they, the provider. A passage that names you once and then refers to you obliquely loses you the moment it is extracted.

Entity profiling compiles attributes about an entity automatically from many sources. Your profile is assembled from everything published about you, most of which you did not write, and consistency across those sources is what makes the profile coherent.

Grounding attaches an identity. Disambiguation decides which identity applies when several are plausible. Grounding without disambiguation produces a well-marked page that a system still resolves to somebody else. The workflow that joins them checks how your name is actually resolved before adding more markup.

Share of model counts mentions across answers. Salience measures whether you were central or incidental in the text that mentioned you. Counting without salience makes a passing reference look like a recommendation. Measuring both shows whether you are being discussed or merely listed.

A local knowledge graph describes your entities. Wikidata carries identifiers that other systems already trust. Connecting the two through identifiers turns a local assertion into a verifiable one. Most companies maintain the first and have never checked the second.

Optimization pursues visibility. Hallucination rate measures fabrication. Raising visibility without measuring fabrication increases the circulation of whatever is wrong about you. Reducing the rate is done by publishing checkable facts in a form that can be verified, which is also what raises visibility.

Citation rate counts references. Attribution accuracy asks whether the reference supports what it is attached to. A rising rate with falling accuracy is worse than obscurity, because your name travels attached to errors. The pair is the only honest reading of citation data.

Schema states what a thing is. A canonical URI states which thing it is, persistently. Schema without a stable identifier produces repeated local descriptions that no system merges. Standardizing the identifier is what turns many descriptions into one entity.

Resolution merges records that describe the same thing. Canonicalization gives the merged thing one identifier going forward. Doing the first without the second means merging the same records again next quarter. Together they are the maintenance procedure.

Parametric knowledge is what the model remembers; non-parametric is what it looks up. Balancing them explains why some corrections take effect immediately and others never do. For a publisher it decides whether the remedy is a page, a public record, or patience.

Retrieval finds a segment. Claim extraction isolates the assertion inside it. Optimizing retrieval alone produces passages that are found and contain nothing extractable. Writing one checkable claim per passage serves both steps at once.

One measures facts per word, the other topic nodes per text. Together they describe what makes material worth compressing: substance per unit of length, in facts and in concepts. Content that scores low on both is exactly the content that generative answers replace entirely.

The pattern is consistent: an assistant describes the company incorrectly, names a competitor with a similar name, attributes somebody else's products, or omits the company from a category it clearly belongs to. In each case the cause is the same, and it is not the website's copy.

Ask assistants who you are and what you do, and compare the answer with what you publish. Check whether a Wikidata entry exists and whether it is correct. Search your own name and look at which other entities appear. That is an afternoon and it usually explains everything.

Make the surrounding context unmistakable: the sector, the location, the founder, the category, stated plainly and consistently wherever you appear. Add sameAs links to authoritative records that carry the same details. Ambiguity is resolved by context, and context is something you can publish.

It helps materially, because many systems consult it and it is one of the few records you can influence directly. It has to be accurate, sourced and maintained rather than promotional. An incorrect entry is worse than none.

It is necessary and it is not sufficient. Markup states what you claim; grounding requires that the claim can be matched against something outside your site. Markup plus consistent external records is what works.

Weeks to months, because records propagate and models update on their own schedule. Corrections to retrieval-based systems appear sooner than corrections to what a model already holds. Expect the first evidence in the citations of answer engines that show their sources.

Make every description of your company identical everywhere it appears: name, category, location, what you do. Inconsistency is the most common reason grounding fails, and it costs nothing but attention to fix.