Search engines no longer function as keyword indexes. Google now combines multiple signals to rank a page: semantic similarity, contextual relevance, topicality, and content freshness. This evolution, documented in Google’s AI systems and Discovery Engine, changes how content should be prepared.
SEO semantic analysis sits at the crossroads of these signals: it aims to structure a text to meet a complete search intent, not just a string of terms.
Hybrid search: why keywords and semantics coexist in SEO
A persistent idea opposes keyword search and semantic search, as if the latter has replaced the former. Recent developments in Vertex AI show the opposite: Google implements a hybrid search combining dense embeddings and BM25 signals. The embeddings capture the overall meaning of a query, while BM25 remains effective for exact phrasing and technical terms.
For concrete semantic analysis, this means two things. The expanded lexical field (synonyms, co-occurrences, related terms) feeds the semantic layer. The precise key phrases, those that users actually type, fuel the BM25 signal. Working on one without the other amounts to half-optimization.
Content on “external thermal insulation,” for example, must cover both the technical vocabulary (rendering, cladding, thermal resistance, thermal bridge) and contain the searched phrases as they are. Building an effective SEO semantic analysis requires mapping these two dimensions before writing, not after.
Entity SEO: structuring content around entities, not words
The most common semantic analysis consists of listing secondary keywords and sprinkling them throughout a text. This approach remains superficial. The framework gaining traction in Google’s systems is called Entity SEO: it involves structuring content around named entities and their relationships, not around simple terms.
An entity, in the sense of the Knowledge Graph, is an unambiguous identifiable concept: a person, a place, a product, a technique. Google associates these entities with each other to understand the thematic depth of a page.
What this changes in practice
Let’s take an article on specialty coffee. A classic approach would target “specialty coffee,” “artisan roasting,” “arabica.” An entity-based approach would identify the relationships between entities like the Specialty Coffee Association, SCA scores, growing regions (Sidamo, Huehuetenango), processing methods (washed, natural, honey). The text then weaves a network of concepts that Google can link to its knowledge graph.
Semantic analysis becomes a relational mapping task, not a simple vocabulary exercise. Traditional semantic analysis tools provide lists of co-occurring terms, but they do not model the relationships between entities. This gap explains why some “well-optimized” content according to semantic scores does not progress in SERP.
Multi-signal scoring: beyond the lexical field
Google’s technical documentation for its Discovery Engine and Generative AI App Builder describes a four-step ranking pipeline: Prepare, Retrieve, Signal, Serve. At the Signal stage, multiple criteria come into play simultaneously.
- Semantic similarity assesses the meaning proximity between the query and the page content, via embedding models.
- Keyword similarity (BM25) measures the raw lexical match with the query terms.
- Topicality gauges the overall thematic coverage of the page concerning the targeted subject.
- Content freshness acts as a complementary signal, particularly for queries related to current events or technical developments.
Content can achieve a good semantic similarity score while failing on topicality if it addresses the subject too narrowly. Conversely, a very broad but poorly targeted article will lose on the BM25 signal. Semantic analysis must anticipate these four dimensions to produce a truly operational writing brief.
Limitations of current semantic tools and areas for improvement
Most semantic analysis tools available on the market operate on a similar principle: they extract frequently used terms from pages ranked in the top 10 for a given query, then calculate a coverage score. This score indicates whether your text contains enough of these terms.
This method has a blind spot. It captures what competing pages have in common, not what differentiates them. It does not model entities, relationships between concepts, or the search intent behind the query. Field feedback diverges on this point: some SEOs achieve significant results by following these scores, while others find that high-scoring content stagnates.
How to complement tool-based analysis
Two approaches can help overcome the limitations of current tools:
- Manually analyze the SERPs to identify recurring entities (proper nouns, technical concepts, standards) and integrate them into the content structure, not just in the body text.
- Cross-reference semantic data with Search Console data to identify high impression but low click queries, which signal a gap between existing content and the actual intent of users.
- Use structured data (Schema.org) to clarify entities and their attributes to search engines, which strengthens the topicality signal.
A semantic tool provides a starting point, not a finalized writing plan. The layer of human analysis remains necessary to transform a list of terms into a coherent content architecture capable of satisfying the multiple ranking signals that Google evaluates simultaneously.
SEO semantic analysis has moved beyond mere lexical enrichment. It now integrates entity logic, multi-signal scoring, and hybrid search. Tools facilitate data collection, but structuring content around this data remains an interpretive task that directly influences SEO results.