Semantic Relevance: How Search and AI Systems Evaluate Meaning

Content can be highly relevant to a query and still fail to rank if retrieval systems don't recognise that relevance. This article covers the mechanisms behind semantic evaluation (knowledge graphs, embeddings, co-occurrence analysis) and their implications for content structure.

What is semantic relevance?

Semantic relevance measures how well content matches the meaning and intent behind a query, not just whether it contains matching words. A search for "apple nutrition" should return content about fruit, not smartphone specifications, even if both pages contain the word "apple."

This distinction between lexical matching (do the words appear?) and semantic matching (does the meaning align?) determines whether content ranks for relevant queries or gets buried despite being useful.

Why lexical matching fails

Early search engines relied on term frequency: count how often a query term appears, weight by document length, return the pages with the highest scores. This approach has an obvious failure mode: a page mentioning "apple" fifty times ranks highly regardless of whether it discusses fruit, technology, or the colour.

More subtly, lexical matching fails in both directions:

• False positives: Pages match queries they don't actually address (the Apple Inc. page ranking for fruit queries)
• False negatives: Pages that address queries don't match because they use different vocabulary ("password recovery" missing a search for "reset login credentials")

The vocabulary mismatch problem is severe. Users and content creators often describe the same concept using different words. A foundational study by Furnas et al. (1987)] found that users chose the same terms for the same concepts only 7–18% of the time, a finding that subsequent research has consistently replicated.

What this means for content (in a nutshell)

Before examining the mechanisms, the practical implications are straightforward: content needs to establish meaning, not just contain words. This requires:

• Entity clarity: Make clear what you're discussing, not just that you're discussing it
• Topical depth: Cover associated concepts, not isolated keywords
• Precise terminology: Include exact identifiers that hybrid retrieval catches
• Clean section structure: Ensure individual chunks remain comprehensible when retrieved in isolation

The sections below explain how these requirements emerge from how systems actually work.

How search engines establish semantic relevance

Google's approach to semantic relevance relies on multiple interconnected systems, each addressing different aspects of the problem.

Knowledge Graph: entity relationships

The Knowledge Graph, introduced in 2012, moved Google from string matching to entity understanding. Rather than treating "mercury" as a text string, the Knowledge Graph recognises it as potentially referring to distinct entities: a planet, a chemical element, an automobile brand, or a Roman deity.

Each entity in the Knowledge Graph has:

• A unique identifier: A machine-readable ID that distinguishes "Mercury (planet)" from "Mercury (element)" regardless of how either is described in text
• Defined properties: Attributes specific to the entity type (orbital period for planets, atomic number for elements)
• Relationships to other entities: Connections that provide context (Mercury the planet relates to "Solar System," "terrestrial planet," "NASA missions")

When processing a query, the system uses surrounding context to determine which entity the user likely means. A query for "mercury thermometer dangers" activates the chemical element entity; "mercury retrograde meaning" activates the astrological concept.

The Knowledge Graph draws from multiple sources: Wikidata, Wikipedia, licensed datasets, and structured data extracted from crawled pages. Schema.org markup provides explicit entity declarations that feed directly into this system.

Co-occurrence and phrase relationships

Before neural embeddings, search engines developed statistical methods to understand semantic relationships. Google's phrase-based indexing system, documented in US Patent 9,990,421, describes mechanisms still relevant to understanding how topical relevance is evaluated.

The system identifies "related phrases" based on their predictive power: how much the presence of one phrase increases the likelihood of another appearing in the same document. The patent describes calculating "information gain" as the ratio of actual co-occurrence to expected co-occurrence.

If "President of the United States" and "White House" appear together 100 times more frequently than random chance would predict, they have high information gain. The patent specifies that phrases must co-occur at least 100 times more frequently than expected to be considered "related" for ranking purposes.

Documents are scored based on which related phrases they contain. The more related phrases present, the stronger the topical signal. This explains why comprehensive content covering associated concepts outperforms thin content targeting isolated keywords.

This statistical approach remains relevant. Co-occurrence data provides a signal based on how humans actually write about topics, complementing what embeddings capture.

Embeddings: meaning as geometry

Vector embeddings represent words, phrases, and documents as coordinates in high-dimensional space. Content with similar meaning occupies nearby coordinates, regardless of surface vocabulary.

The embedding for "automobile" sits close to "car," "vehicle," and "sedan" because these words appear in similar contexts across training data. A query embedding for "best family vehicle" will have high similarity scores against content about minivans, SUVs, and sedans—even if those pages never use the word "vehicle."

Embeddings address the vocabulary mismatch problem directly. Two documents can use entirely different words while occupying the same semantic region.

Google has deployed several embedding-based systems:

Each improved on the last. BERT, for instance, recognises that "bank" in "river bank" differs from "bank" in "bank account" based on surrounding words. This distinction is impossible with isolated word embeddings.

Semantic relevance in AI retrieval

RAG systems use semantic relevance as their primary retrieval mechanism. When an LLM needs external information to answer a query, it searches using embeddings rather than keywords.

The process works as follows:

1. User query is converted to a vector embedding
2. The embedding is compared against pre-computed embeddings for all indexed content
3. Content with the highest similarity scores is retrieved
4. Retrieved content is passed to the LLM as context for generation

This makes semantic relevance the gatekeeper for AI visibility. Content that doesn't score well on embedding similarity won't be retrieved, regardless of other qualities.

How semantic relevance differs across systems

Different systems apply semantic relevance differently:

• Traditional web search: Combines entity recognition, co-occurrence patterns, and link analysis. Relevance signals include Knowledge Graph associations, related phrase coverage, and authority indicators.
• RAG retrieval (AI systems): Relies primarily on vector embeddings and semantic similarity. Operates at the chunk level: passages are retrieved based on embedding proximity, then evaluated for information useful to the specific query.
• E-commerce search: Emphasises attribute matching and behavioural signals. Product taxonomy, purchase patterns, and query-product associations weight heavily alongside semantic similarity.

Where semantic-only retrieval fails

Pure semantic search has a weakness that matters for commercial content: it can miss specificity. A query for "Bosch 18V cordless drill model GSB18V-490" might retrieve general content about cordless drills because the embedding captures "cordless drill" more strongly than the specific model number.

Brand names, product codes, technical specifications, and proper nouns don't always embed distinctively. The embedding for "GSB18V-490" may not encode meaningful product-specific information; it's just a string of characters that the model hasn't learned to differentiate.

This is why production RAG systems use hybrid retrieval, combining semantic search with BM25 for keyword matching. BM25 is a term-frequency algorithm (a refined version of TF-IDF, which weights terms by how often they appear in a document relative to how common they are across all documents). Content needs to satisfy both retrieval modes: semantically relevant to the topic and containing the specific terminology users search for. See Terminology precision for what types of terms require exact-match presence.

Schema markup and semantic signals

Structured data (schema markup) provides explicit semantic signals, but its relationship to relevance is frequently misunderstood.

What schema markup does

Schema markup declares entity types, properties, and relationships in machine-readable format. A Product schema explicitly states: "This page describes a product with these attributes." This removes ambiguity that text parsing might introduce.

What we know about schema's impact

Confirmed effects:

• Rich result eligibility: Certain schema types (Product, Review, FAQ, HowTo) enable enhanced SERP features
• Knowledge Graph integration: Organisation, Person, and LocalBusiness schema can contribute to Knowledge Panel information
• Entity disambiguation: Schema explicitly declares which entity type a page discusses, reducing ambiguity

What's uncertain:

• Direct ranking impact: Google has stated schema is not a direct ranking factor, but "can help us understand your page better." Whether better understanding translates to ranking benefits is not explicitly confirmed.
• Semantic relevance signals: Whether schema influences how Google evaluates topical relevance (beyond enabling specific features) is unclear.

What's likely overstated:

• Claims that schema "helps Google understand meaning" often overstate the case. Google's natural language processing can extract entities and relationships from well-written content. Schema provides explicit confirmation, not revelation.
• The idea that more schema types automatically improve relevance. Schema should match actual page content; adding inapplicable schema creates inconsistency.

Measuring semantic relevance

Evaluating semantic relevance is more complex than counting keyword matches. Several approaches exist:

Embedding similarity scores

The most direct measure: compute the cosine similarity between query and document embeddings. Scores range from -1 to 1, with higher values indicating greater semantic alignment.

In practice, relevant content typically scores above 0.7, though thresholds vary by domain and embedding model. A page about "Python programming" might score 0.85 against a query about "writing Python scripts" but only 0.3 against "snake species."

Topical coverage metrics

Rather than single-score similarity, topical coverage examines whether content addresses the concepts a query implies. A comprehensive guide to "starting a business" should cover entity formation, licensing, funding, and operations; not just one aspect.

Tools that analyse top-ranking content for topic clusters are measuring this form of semantic completeness. The logic: if competing pages consistently cover certain subtopics, those subtopics are semantically expected for the query.

Entity co-occurrence analysis

For entity-centric queries, relevance can be assessed by whether content mentions expected related entities. A page about "Tesla" (the company) should co-occur with entities like "Elon Musk," "electric vehicles," "Model 3," and "Gigafactory." Missing expected entities may signal incomplete semantic coverage.

Practical implications for content

Understanding how semantic relevance works suggests specific content approaches:

Mapping your domain before creating content

Before creating content to establish semantic relevance, map the entities and relationships that define your domain. This prevents scattered content that covers topics without establishing coherent connections.

A domain map identifies:

• Core entities: The products, services, concepts, or topics you want to be associated with
• Entity relationships: How entities connect (product X solves problem Y, service A integrates with platform B, concept C is a type of category D)
• Terminology variations: The different terms users and industry sources use for the same concepts

This mapping exercise reveals gaps and priorities. If your domain includes "enterprise CRM" as a core entity, your content should establish relationships to associated entities: sales pipeline management, customer data platforms, integration with marketing automation, comparison with specific competitors. Each relationship suggests content opportunities.

The practical output is a reference document listing entities and their expected associations. When creating new content, check whether it reinforces existing entity relationships or introduces new ones. Content that doesn't connect to your entity map may still be useful, but it won't strengthen your semantic footprint for the topics you've prioritised.

This approach resembles keyword research but focuses on concepts rather than individual terms. Rather than targeting individual search terms, you're establishing the conceptual territory your content should occupy. The specific keywords follow from the entity relationships, not the other way around.

Entity clarity

Ambiguous entities create retrieval problems. If your content discusses "Mercury," is it the planet, the element, the automobile brand, or the Roman god? Without explicit signals, systems must guess—and may guess wrong.

Disambiguate through:

• Context terms: Surround entity mentions with clarifying vocabulary
• Schema markup: Explicit entity type declarations (@type: Organization, @type: Planet)
• Consistent naming: Use full, unambiguous entity names rather than abbreviations

Topical comprehensiveness

Semantic relevance rewards depth. A page covering multiple aspects of a topic generates embeddings that align with more query variations than a page covering one narrow aspect.

This doesn't mean padding content with tangentially related information. It means genuinely addressing the concepts users associate with a topic:

• For a product page: specifications, use cases, comparisons, compatibility
• For an informational guide: definitions, methods, examples, edge cases
• For a service page: process, outcomes, qualifications, scope

Terminology precision

While semantic search handles vocabulary mismatch, exact terminology still matters; particularly for:

• Brand names and trademarks
• Product models and version numbers
• Technical specifications and standards
• Industry-specific jargon

These identifiers may not embed meaningfully, but hybrid retrieval systems catch them through keyword matching. Content that uses precise terminology alongside natural language descriptions performs better across both retrieval modes.

Information structure

RAG systems chunk documents before embedding. A 3,000-word article becomes multiple chunks, each embedded and retrieved independently.

Chunking strategies vary, but most split on structural boundaries: paragraphs, sections, or semantic units. This means section-level coherence matters:

• Each section should address a focused subtopic
• Sections should be comprehensible in isolation (the retrieval system may only see one chunk)
• Key information should appear early in sections rather than buried in later paragraphs

A section titled "Return Policy" that actually discusses shipping for three paragraphs before addressing returns will produce a misleading embedding. The chunk may be retrieved for return-related queries, then disappoint the LLM when it doesn't contain return policy information.

Common misconceptions

"LSI keywords" improve semantic relevance

The term "LSI keywords" (Latent Semantic Indexing keywords) has circulated in SEO for years, typically referring to related terms that should be added to content. The concept is questionable on multiple levels:

• LSI is a specific matrix factorisation technique from 1988, not a keyword research method
• Google has stated they don't use LSI
• Tools claiming to provide "LSI keywords" typically generate related terms through other methods (autocomplete, co-occurrence analysis, embedding similarity)

Adding topically related terms can improve semantic coverage, but the mechanism isn't "LSI"; it's providing embeddings with richer semantic signal and satisfying user intent more completely.

Schema markup guarantees semantic understanding

Structured data provides explicit entity signals that can feed into knowledge graphs. It does not guarantee that content will be understood correctly or retrieved for relevant queries.

Schema markup is one input among many. If page content contradicts the markup, or if the markup is implemented incorrectly, systems may ignore it. For AI retrieval specifically, the evidence that schema markup influences citation is inconclusive.

Semantic SEO is separate from "regular" SEO

Semantic relevance isn't a distinct discipline; it's how search has worked for over a decade. The Knowledge Graph launched in 2012. RankBrain launched in 2015. BERT in 2019.

Content that ranks well today already satisfies semantic relevance criteria, whether the creator was consciously targeting them or not. "Semantic SEO" as a separate practice typically means making these implicit requirements explicit and systematic.

Keyword density still matters

Modern systems don't count keyword occurrences in the way older algorithms did. A term appearing once in a relevant context can signal more than repeated occurrences in thin content. Semantic relevance is about meaning alignment, not term frequency.

FAQs

How do I know if my content is semantically relevant?

Test against query variations. If your content about "project management software" also ranks for "team collaboration tools," "task tracking apps," and "workflow management platforms," it's semantically relevant to that topic space. If it only appears for exact-match queries, the semantic signal may be narrow.

Does semantic relevance replace keyword targeting?

No. Use both. Include specific terms (brand names, model numbers, technical specs) for keyword matching alongside comprehensive topic coverage for semantic retrieval. See Terminology precision for what types of terms require exact-match presence.

How does semantic relevance differ between Google Search and AI Overviews?

The underlying mechanisms overlap significantly: both use embeddings and entity recognition. However, AI Overviews involve a generation step where retrieved content is synthesised into a response. Content that's semantically relevant may be retrieved but not cited if it doesn't support the specific claims the system generates. See how citations get attached for details.

Should I optimise differently for semantic relevance in AI systems?

The fundamentals are shared: entity clarity, topical depth, precise terminology, and clean information structure benefit both traditional search and AI retrieval. The main AI-specific consideration is chunk-level coherence, ensuring that individual sections of content remain useful when extracted in isolation.

How do embeddings handle content that's been updated?

When content changes, it needs to be re-crawled and re-indexed for new embeddings to be generated. There can be a lag between content updates and updated embeddings in retrieval systems. Significant content changes may not immediately affect retrieval performance.

Key takeaways

1. Semantic relevance measures meaning alignment, not word matching: Systems evaluate whether content addresses query intent, not just whether it contains query terms

2. Multiple mechanisms work together: Knowledge graphs handle entity disambiguation, embeddings capture meaning similarity, co-occurrence provides statistical grounding based on how topics are actually discussed

3. Hybrid retrieval requires both semantic and lexical signals: Pure semantic search misses specific terminology; production systems need content that's both topically relevant and terminologically precise

4. Schema markup enables features but doesn't directly boost rankings: Implement schema for types that enable specific features; don't expect it to substitute for comprehensive, well-structured content

5. Section-level coherence matters for AI retrieval: RAG systems chunk content before embedding; each chunk should be comprehensible and topically focused in isolation

Further reading

• The Vocabulary Problem in Human-System Communication (Furnas et al., 1987)
  Foundational research showing users choose the same terms for concepts only 7–18% of the time
• Phrase-based searching in an information retrieval system (US9990421B2)
  Google patent describing co-occurrence analysis and related phrase identification for document ranking
• Google's Knowledge Graph overview
  Original 2012 announcement explaining how Google moved from "strings to things"
• BERT explained (Google AI Blog)
  Technical introduction to the embedding model that transformed query understanding
• Schema.org documentation
  Reference for structured data vocabulary used in entity markup
• Understanding embeddings (Google ML Crash Course)
  Accessible introduction to how vector representations encode meaning