Use Relevant Entities and Semantically Related Words
Koray Tuğberk Gübür emphasizes the importance of using appropriate entities (such as people, organizations, laws, and places) and semantically related words (synonyms and related concepts) to deepen content and improve how search engines understand it. This rule is one of the core pillars of writing content aligned with contextual SEO and Google AI Mode.
Introduction
In Semantic Content Writing, the strength of content doesn't depend on grammatical correctness alone — it depends on how much it includes entities and specialized terminology that give the text precise meaning and clear context. A sentence using generic wording may be grammatically correct, but it provides limited semantic signals, while specific entities help build content that is clearer and more connected to the topic.
For example, the meaning differs between the phrase "I went to the doctor" and "I went to a nutritionist." In the second example, it isn't just a profession being mentioned — it names a specialized entity that search engines and AI systems can tie to a specific knowledge domain, which reduces ambiguity and strengthens contextual understanding more precisely.
The use of entities isn't limited to the names of people or organizations — it also includes products, devices, medications, standards, scientific terms, and other concepts related to the field. Combining these entities with semantically related terms gives content real topical depth, and helps search engines and AI systems link the page to knowledge graphs and entity graphs, which strengthens understanding of the page's topic and increases its chances of appearing in advanced search results such as AI Overview.
Why Does This Rule Matter?
Enhances Precise Semantic Coverage (Microsemantics)
Using specific entities and terminology covers fine-grained aspects of a topic that generic phrasing never reaches.
Demonstrates a Deep Understanding of the Topic
Mentioning the correct entities proves genuine domain knowledge, not just surface-level writing.
Increases Chances of Appearing in Themes and AI Summaries
Entity-rich content is more citable within AI-generated summaries.
Helps with Classification Within the Knowledge Graph and Entity Graph
Clear entities make it easier for Google to link the page to the topic's knowledge network.
Key Concepts
| Concept | Definition | Example |
|---|---|---|
| Entity | A recognized name tied to a topic: a person, organization, product, place, law... | Ministry of Health, Vitamin D, diabetes |
| Semantically related word | Terms or concepts directly or indirectly related to your topic | Diet ↔ calories, metabolism, obesity, nutrition |
Comparison Examples
Compare generic phrasing with its counterpart rich in entities and semantically related words:
| Topic | Incorrect phrasing | Improved phrasing |
|---|---|---|
| Meal planning | I went to the doctor to get a diet. | I went to a nutritionist to get a suitable meal plan. |
| Benefits of exercise | Exercise is good for your body's health. | Exercise improves heart health, muscular strength, and mental well-being. |
| Investment options | You have several investment options. | You have several options such as stocks, bonds, mutual funds, and real estate. |
Practical Application Method
Identify the core entities of your topic
Such as devices, diseases, cities, laws, or any specific entity tied to the content.
Use related words that semantically enrich the text
Without literal repetition — instead, bring in concepts and terms genuinely relevant to the topic.
Blend the terms smoothly into the paragraph
Keep the text's natural feel, and don't force entities in artificially.
Check the context
Only use a word if it's genuinely appropriate for the subject at hand and truly related to it.
Table 1: Entities and Related Words by Field
A comprehensive reference by field, showing the core entities and semantically related words for each domain:
| Field | Entities | Semantically related words |
|---|---|---|
| Nutrition | Nutritionist, calories, Ministry of Health | Diet, metabolism, vitamins, cholesterol |
| Education | Teaching strategy, teacher, curricula | Active learning, formative assessment, collaborative learning |
| Finance | Stock market, central bank, deposits | Returns, bonds, financial planning, savings |
| Mental health | Anxiety, depression, therapy sessions | Stress, mental healthcare, behavioral support |
| Renewable energy | Solar energy, wind, energy policies | Solar panels, turbines, energy efficiency |
Editorial Tips
Don't use a generic word — pinpoint the entity precisely
Don't settle for a word like "medication" — specify: "a second-generation antidepressant medication."
Tie every concept to a word that clarifies its function or result
This gives the entity a clear functional context instead of mentioning it in the abstract.
Apply this rule in nearly every paragraph
To feed AI systems enough context to help them understand the topic in depth.
A Case Study and Real-World Improvement
A complete example showing how a generic paragraph is transformed into one rich with entities and semantic words:
| Before | After |
|---|---|
| There are several ways to maintain a healthy lifestyle. Exercise, eat well, and drink water. |
Maintaining a healthy lifestyle includes 7 scientifically proven practices, among them:
|
🟢 Clear entities were added (exercise, diet) plus semantic words (heart, protein, metabolism), making the paragraph semantically richer and easier to index.
Conclusion
The "Use Relevant Entities and Semantically Related Words" rule means replacing every generic phrase with a specific entity or term that reflects genuine knowledge of the topic, and enriching every paragraph with concepts functionally tied to the main idea.
A professional writer uses entities as semantic anchors, expands the paragraph with words related to the function or field, and always ties the category, function, and result together within the sentence. This semantic enrichment is what makes content deeper and more credible, and easier for Google and AI systems to understand and link to the topic's entity network.
