pConceptMap2025-09-08

from A Framework for Constructing Concept Maps from E-Books Using Large Language Models: Challenges and Future Directions pConceptMap2025-09-08

• Concept Map

memo https://chatgpt.com/c/68ba96f1-2650-8320-84a6-33bb3f7838ae

4 stages: (1) sectioning → (2) concept extraction → (3) relationship identification → (4) integration

• Labels for extracted concepts
• Identification of the relationship between them
• This is the trend.Keichobotにも関係しそうだ

Summary of experimental results: 📊 Generated concepts (5):

• globalization - core
• digital technology - core
• democracy - core
• plurality - core
• collaboration - supplementary

🔗 Relationships between concepts (4):

• globalization → digital_technology (prerequisite_of, 0.90)
• democracy ← collaboration (part_of, 0.85)
• digital_technology → plurality (example_of, 0.80)
• democracy ↔ globalization (contrasts_with, 0.75)

I understood the approximate flow.

• First, "keywords that seem important" are extracted from the given document,
• Then give that keyword list and a set of documents to extract the "relationships".

cost consideration

• Now gpt-4o-mini

• The fee for developers is 15 cents per million input tokens and 60 cents per million output tokens (equivalent to about 2500 pages in a standard book).... . ChatGPT will allow Free, Plus, and Team users to access GPT-4o mini, which replaces GPT-3.5, starting today.

  • https://openai.com/ja-JP/index/gpt-4o-mini-advancing-cost-efficient-intelligence/
• https://openai.com/ja-JP/api/pricing/
  • GPT-5, mini, nano
    • $10, $2, $0.4 in output

GPT-5-mini vs. other model comparison Features of the 🏆 GPT-5-mini 📊 Degree of detail of concept:.

• More detailed and precise concept definition
• Extract even the finest elements: ⿻ (Unicode symbol).
• Compound understanding combining Japanese and symbols 🔗 Accuracy of the relationship:.
• Six relationships are extracted (other models have about four)
• High confidence (0.90-0.95)
• Interrelationships are also captured (uses ↔ example_of).

I'd like to synthesize the two-way arrows because it's hard to see them overlapping.

• It's a pre-LLM idea to extract a relationship like part_of in the first place, let the relationship be explained in words too.

• Do not symbolize the relationship.

• Text-based relationship extraction

• relationship is meaning and vice versa

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