Google and search engines have radically evolved over the years: they now truly understand what users need, and with the introduction of AI Overviews, these needs are met through immediate, personalized responses.
Today's search engines reward content that demonstrates genuine, comprehensive understanding of topics—content that anticipates users' related questions and naturally connects to other relevant concepts.
Semantic SEO isn't a new concept, but rather an approach that continuously evolves with changing search behaviors. It allows you to align your content with how Google and artificial intelligence interpret searches, boosting your chances of appearing in traditional results, AI-generated conversations (through what's known as GEO - Generative Engine Optimization), and all contexts that deliver direct answers to users.
In this guide we will talk about how to concretely implement this approach, from understanding semantic search mechanisms to operational techniques.
Contents:
What is semantic search and how does it work
Semantic search is how search engines analyze the meaning behind words, not just the words themselves. They use Natural Language Processing (NLP) and machine learning techniques to interpret search intent and context.
How it works technically
Query analysis: The process of entity recognition (mini excavator = construction machinery, urban projects = specific operational context) and intent analysis (B2B commercial search for construction equipment) allows the decoding of the query's real meaning.
Knowledge Graph integration: Google connects 'mini excavator' to other related concepts in its database: earthmoving, controlled demolitions, precision excavations, urban construction site regulations, CE certifications, rental vs purchase.
Content analysis: It analyzes web pages not just for 'mini excavator urban projects' but for related topics, technical specifications, construction site case studies, and site authority in the construction sector.
Practical example:
- Traditional search: 'mini excavator for urban projects' finds pages with exactly these words.
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Semantic search: understands you're looking for machinery for construction sites with specific constraints and shows you Bobcat, Kubota, CASE IT models, rental prices, questions about hourly costs ('How much does an hour of mini excavator work cost?'), necessary documentation, best brands, configurations in different tonnages/quintals.
This process allows Google to offer more complete and useful results, showing not just products but also guides, comparisons, and related information that solve doubts you didn't even know you had.
Semantic technologies: the foundation for optimization
What is Natural Language Processing (NLP)
Natural Language Processing is the technology that teaches computers to 'read' like humans. Instead of seeing just separate letters and words, computers learn to understand the complete meaning of sentences.
Take 'mini excavator urban projects': while traditional analysis only processes character strings, NLP decodes meaning and semantic relationships. NLP interprets 'mini excavator' as specific construction machinery and 'urban projects' as a constrained operational context, allowing Google to infer B2B commercial intent for specialized equipment.
What is Entity Recognition
Entity recognition is how Google identifies the specific 'things' you're talking about. It no longer sees generic words, but identifies precise elements and categorizes them.
If you write 'The Bobcat E20 is ideal for excavations in historic centers,' Google recognizes:
- Bobcat = earthmoving machinery brand
- E20 = specific mini excavator model (2 tons)
- historic center = operational context with regulatory constraints
Google instantly identifies entities: it recognizes 'Bobcat' as a manufacturer, 'E20' as a specific model in the 1.5-2.5 ton range, and 'historic center' as a regulated operational environment with restricted spaces. This immediate classification allows the search engine to build precise semantic connections.
What is the Knowledge Graph
The Knowledge Graph is Google's massive encyclopedia containing billions of pieces of information about people, places, objects, and concepts—and most importantly, how they're all connected.
Technically, it functions as a relational database of interconnected entities, where each node represents a concept with defined attributes and relationships.
Google consults the Knowledge Graph and finds all connections:
- Brands: Bobcat, Kubota, CASE, New Holland, Yanmar, Hitachi...
- Technical specifications: tonnage (1-3t), extendable arm, rubber tracks...
- Operational contexts: historic centers, underground utilities, controlled demolitions...
- Regulations: CE certifications, operator licenses...
- Commercial models: rental, sales, operational leasing…
These connections become the benchmark for evaluating thematic completeness: content that touches multiple Knowledge Graph categories demonstrates real competence, while simple keyword repetition without context is easily identified as superficial.
What is BERT
BERT (Bidirectional Encoder Representations from Transformers) is the algorithm that taught Google to understand word context. Before BERT, Google read sentences from left to right like we do. BERT instead reads each word by looking at both what comes before and after, understanding how each word influences the meaning of others.
The practical impact of BERT
BERT is why you now need to write naturally instead of forcing keywords. Google perfectly understands 'compact machine for excavations in tight spaces' even if it doesn't contain 'mini excavator urban projects.' In fact, it often rewards natural, varied language more than mechanical keyword repetition.
This is also why voice and conversational searches work so well: BERT understands complete questions like 'Which excavator can I use in a 200-square-meter construction site in the city center?' transforming them into precise intents to deliver relevant results.
The multimodal evolution: MUM
Google didn't stop at BERT. In 2021, it introduced MUM (Multitask Unified Model), capable of simultaneously interpreting text, images, and video in 75 different languages.
The practical difference? You can photograph a pipe that needs repair in a basement and ask 'which mini excavator could handle this type of job?' - MUM analyzes the image, evaluates necessary depth, maneuvering space, and terrain type, retrieving relevant information even from German technical manuals or Japanese demonstration videos.
For semantic strategy, this means not limiting yourself to text: images must have descriptive alt text that contextualizes specific use. Videos need transcripts that specify the type of work shown, infographics must clearly distinguish between 1-3 ton mini excavators for small spaces and 50+ ton excavators for quarries and large construction sites. Semantic consistency across all formats, adapted to specific operational context, is what distinguishes superficial content from authoritative content.
Implementation of Semantic SEO
Now that we understand how search engines interpret queries, it's time to translate this knowledge into concrete operational strategies.
Semantic SEO means shifting from mechanical optimization of isolated keywords to building information architectures that mirror how Google and generative AI map knowledge.
From Keyword Research to Semantic Research
Traditional keyword research considers specific terms to optimize page by page. Semantic research instead maps complete thematic ecosystems that Google and generative AI consider interconnected.
A thematic ecosystem is the collection of all concepts, questions, and subtopics that revolve around your main topic. You're no longer searching for individual words, but understanding the entire semantic universe of a topic.
What are topic clusters
These thematic ecosystems organize into topic clusters: groups of interconnected content structured around a central topic. A cluster is like a content family where each member addresses a different aspect of the same main theme.
The importance of Content Depth
To excel in semantic search, you must cover various themes connected to your main topic with genuine content depth creating the so-called topical authority. This approach serves a dual purpose: search engines recognize you as an authoritative source for the entire topic cluster (one of the main ranking factors), and generative AI systems have richer, high-quality material to reference and cite when producing answers.
The logic is the same for Google, ChatGPT, Claude, or other AI systems: they reward those who demonstrate comprehensive expertise, not surface-level coverage.
How to build topic clusters
Start from your main topic and add all related themes, from most general to most specific:
- Level 1 - Core variations: mini excavator, compact excavator, electric excavator
- Level 2 - Technical subtopics: tonnage (1-3t), extendable arm, rubber/steel tracks
- Level 3 - Use cases: urban construction sites, historic centers, underground utilities, internal demolitions, pool excavations
- Level 4 - Operational aspects: rental, leasing, operator licenses, hourly costs, municipal regulations
In addition to free and paid SEO tools, Google’s “People Also Ask” (PAA) section is extremely valuable for identifying what should be included in your cluster.
Semantic Content Structure
The structure of your content is fundamental to clearly communicate the semantic organization of your topic. You need to create a logical hierarchy that reflects how the different elements connect naturally to one another.
Semantic H-Tag Structure
HTML headings (H1, H2, H3) should follow a clear topical logic. Each level should represent a natural deep dive into the subject.
H1: Excavators: A Complete Guide from Selection to Use
- H2: How are excavators classified?
- H3: Classification by tonnage and power
- H3: Differences between crawler, wheeled, and spider excavators
- H2: Mini excavators for tight spaces (1–5 tons)
- H3: Which tracks are best for urban construction sites?
- H3: When is zero-tail rotation required?
- H2: Medium excavators for building projects (6–20 tons)
- H3: Digging depth and reach: how to calculate your needs
- H3: Digging depth and reach: how to calculate your needs
- H2: Heavy excavators for large-scale works (20+ tons)
- H3: Quarries and mining: which configuration?
- H3: Industrial demolition vs earthmoving
The transformation of some headings into direct questions is not accidental. This structure mirrors the way users search on Google and generative AI, especially via voice search. AI Overviews tend to select content that directly answers specific questions, because it’s easier to extract and synthesize.
Semantic Internal Linking
Internal links create logical semantic connections between related content. They should guide users through a coherent topical journey, not just distribute link equity.
Examples of semantic linking:
- From the article “Guide to Choosing a Mini Excavator for Urban Construction Sites” → to the product category “Mini Excavators 1–3 Tons”
- From the guide “How to Calculate the Required Digging Depth” → to product pages for excavators with detailed technical specifications
- From the tutorial “How to Choose the Right Excavator Tracks ” → to the product filter “Rubber-Tracked Excavators”
Strategic Schema Markup
Schema Markup is the structured language you use to “explain” to Google exactly what your content is about. It works like providing a detailed dictionary that describes every element of your page: topics, entities, author expertise.
Schema Markup is fundamental because it acts as the direct bridge between your content and Google’s Knowledge Graph, which we explored in the previous chapter.
When you implement it, you are literally presenting your entities to the Knowledge Graph in a format Google can easily understand and integrate — combining technical SEO and semantic optimization into a single strategy.
Simplified example of Schema Article
This Schema clearly communicates that the article is about “excavators” as a product within the “earthmoving machinery” category. It helps Google correctly connect the content to the related entities in the Knowledge Graph.
Interconnected Schema Markup
Schema.org allows you to link different types of markup to create semantic relationships between the content on your site — and even with external sites. For example, an article can reference specific products, which in turn connect to your organization.
According to Google’s structured data guidelines, consistent and interconnected markups help the search engine better understand the relationships between your content.
This interconnection enables Google to map the relationships between your articles, products, and organization, building a more complete picture of your online presence in your specific industry.
Conclusions
Semantic SEO is not a new concept, but an approach designed to better interpret user needs, deliver complete answers, and position your content as the most relevant solution.
It’s not just a tactic for improving rankings — it’s a strategy that allows you to communicate clearly and in a structured way what you can offer, creating value both for your business and for users searching for answers.
Implementing semantic SEO correctly requires specific expertise and a significant investment of time. But this is precisely the kind of investment that allows you to build a robust information architecture — one that can adapt to any future evolution in search, whether through traditional engines, generative AI, or the next wave of innovations.
