📚 1. Define Your Authors’ Definition Based on Semantic Closeness

🔹 An author represents the reputation of the main content creator.
The main content creator is the name behind the content, while the website represents the source of the content.
Together, the Main Creator and Source establish the baseline quality and authority for a subject.

🔍 Google's Approach to Authors:

• Google has started to highlight authors directly as content sources in the Articles Carousel and on the web source panel.

• Example: Barry Schwartz is shown as the author of articles from authoritative sources.

• Google also displays authors in the “About This Result” panel to reinforce authority.


• 🔸 Matt Cutts previously mentioned that Author Authority (or Rank) is used for in-depth articles.

🧠 Google’s View on Content Creator & Source Reputation

• Google separately defines the reputation of:
  💼 The content creator (author)
  🌐 The source (website)
• Both are connected semantically for better quality signals.

✍️ How to Define an Author Using Semantic Closeness

🔸 Semantic Closeness = How contextually relevant the author is to the topic.

💡 Example:
For a topic like Formula 1, relevant authors could be:

• 🏎️ A Car Engineer
• 🧑‍✈️ A Professional Driver
• 💨 A Speed Enthusiast

🔍 The closer the author’s occupation is to the subject, the stronger the authority signal.

📈 Real-World SEO Case Study: Mango Languages

• Every author is defined as a Linguist 🧠 because the project is centered on Language Learning, Explanation, and Research.
• Author definitions are consistent based on semantic closeness.
• Authors are connected to the brand via a simple sub-folder structure.
• Most authors are designed under one entity type — Linguist.

🕵️‍♂️ Make Your Authors Searchable – Even for Micro-Trends

✅ Example Author Profile:

George Smith is directly defined as a Linguist.
🎓 Holds a Ph.D. in Second Language Studies from the University of Hawai‘i at Mānoa.
🗣️ Researches language listening, speaking, and vocabulary learning.

⚠️ Many SEOs believe a long bio makes an author look more authoritative — this is a misconception.

🔍 Focus on Semantic Parsing in Author Bios

👉 Example attributes of language-related content:

• Listening 👂
• Speaking 🗣️
• Vocabulary 📘
• Teacher 👨‍🏫
• Learner 📖
• Researcher 🔬
• University 🎓
• Study 🧑‍🎓

💡 Create bigrams for stronger semantic signals:

• Language–Study
• Language–Vocabulary
• Language–Listening
• Language–Teacher

✅ A Linguist becomes the most semantically relevant noun that binds all these concepts.

⚙️ Author Relevance in Search Algorithms

📊 Ranking algorithms can be:

• Quantitative – e.g., check occupation and expertise titles.
• Qualitative – analyze content depth and author alignment.

🔸 Using occupation names and expertise terms helps algorithms assess the author’s relevance and credibility efficiently.

📌 Match Occupation to Subject for Better Semantic Closeness

🔑 What Does Semantic Closeness Really Mean?

🧠 It's about the distance between terms in a semantic chain.

Example:
Language Learning → Language Study → Language Science → Linguist
➡️ The distance is 2 from “Language Learning” to “Linguist”.
📈 Shorter distance = higher semantic relevance.

🔍 Search engines dealing with billions of AI-generated pages daily can’t always verify every article — so quantitative, semantic audits help reduce the load.