This document discusses optimizing websites and search engines using semantic techniques. It suggests that Website B, with more content, triples, accuracy and connected topics, would be more successful at satisfying search queries. It introduces the concept of topical authority to lower retrieval costs. Several techniques are proposed for language model optimization including fine-tuning, creating topical maps and semantic networks, and generating content informed by human effort and microsemantics. Cross-lingual embeddings and understanding word relationships are also discussed as ways to improve semantic search.

1. Cost of Ranking A Website can’t be higher
than Cost of Not Ranking the Website.
Koray Tuğberk GÜBÜR, 3 March, 2023, Saigon

2. Which one is Cheaper?
Website B
1400 Content Item (Web Pages)
1200 Triples
94% Accuracy
7 Connected Topics
Clarity %96
Website A
600 Content Item (Web Pages)
900 Triples
59% Accuracy
3 Connected Topics
Clarity %78

3. To satisfy 9 Million Search Queries
I won’t rank 9 million different website.

4. Topical Authority is born from the idea
of Decreasing the Cost of Retrieval.

5. Have Query Responsiveness

6. Prepare for Future Generative AI Search

7. LLMo Optimization for SEO
1. Fine-tune a LLM.
2. Create a Topical Map.
3. Create a Semantic Content Network.
4. Generate Content
5. Include Human Effort
6. Improve your Knowledge Base
7. Make your website a Speaking AI.
Human Effort with Microsemantic Optimization is must.

8. Find a Knowledge Base

9. Verbalize the Knowledge Base

10. Verbalize the Knowledge Base

11. Use Natural Language Inference with
Chain of Reasoning

12. Semantics are Language Agnostic.
Source: Facilitating communications with automated assistants in multiple languages

13. Cross-lingual Embeddings for Semantic
Search
A Cross Lingual Embeddings Example for the Sentence
‘Semantic search is an opportunity for Conversational Generative AI Search.’

14. Some Research Papers and Patents from
Google
Word Embeddings with Semantic Distance and Context Associations.

15. Some Research Papers and Patents from
Google
Word Embeddings with Semantic Distance and Context Associations.

16. Some Research Papers and Patents from
Google
Word Embeddings with Semantic Distance and Context Associations.

17. Some Research Papers and Patents from
Google
David C. Taylor – Context and Knowledge Domains with Embeddings

18. Some Research Papers and Patents from
Google
Hexagon is connected to Ultrasonic.
Ultrasonic is not a primary connection to Hexagon.

19. Some Research Papers and Patents from
Google
«Hexagon» is an «Ultrasonic Wave» type.

20. Some Research Papers and Patents from
Google
‘Quartz’ and ‘Cleaner’.
Macro Context: ‘Industrial Ultrasonic Cleaning Machines‘.

21. How to Understand Language Models and
Semantic Connections within them
‘Quartz’ and ‘Cleaner’.

22. How to Understand Language Models and
Semantic Connections within them
‘Quartz’ and ‘Ultrasonic’.
• ‘Quartz’ and ‘Ultrasonic’.
• Macro Context: ‘ Quartz Ultrasonic
Absorption and Measurement‘.
• Micro Context: Ultrasonic Cleaning for
Qaurtz Surfaces
• Zertec and its Products is signifier.

23. What is Large Language Model Optimization?
A Language Interpretability Tool by Google is published. You can check ‘Word Compositionality’ and ‘Embeddings’
for certain contexts.

24. How to Understand Language Models and
Semantic Connections within them
Large Language Model Optimization and Answer Engine Optimization are different technical expressions for
Semantic Search Engine Optimization.
Sequence Modeling (Word Compositionality Modeling)
İs backbone of Semantic SEO.
• What is the possibility of ‘Cat’ appears with predicates of
‘chase’, ‘eat’, or ‘fly’?.
Optimize Sequences of Words. (Sequence Modeling).

25. An Example of Relevance Configuration
‘Financial Advisor helps families to achieve financial independence.’ ‘Families achieve financial independence with the help of the financial
advisor.’
Macro Context: ‘Financial Advisor’ Macro Context: ‘Family Economics’
Possible Search Queries: ‘Financial Advisor + Family’
Possible Representative Question: ‘What does
Financial Advisor help families for?’
Possible Search Queries: ‘Family + Financial Independence’
Possible Representative Question: ‘How does a
family achieve financial independence?’

26. How to Understand Language Models and
Semantic Connections within them
‘Purple Yam’ -> ‘Sweet Potatoes’

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