RRepoGEO

REPOGEO REPORT · LITE

RManLuo/Awesome-LLM-KG

Default branch master · commit 3ae1919c · scanned 5/28/2026, 4:48:40 PM

GitHub: 2,595 stars · 179 forks

AI VISIBILITY SCORE
15 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface RManLuo/Awesome-LLM-KG, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Refine 'About' description to emphasize 'awesome list' nature

    Why:

    CURRENT
    Awesome papers about unifying LLMs and KGs
    COPY-PASTE FIX
    A curated awesome list of papers and resources on unifying Large Language Models (LLMs) and Knowledge Graphs (KGs).
  • highlicense#2
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Add a standard open-source LICENSE file (e.g., MIT, Apache-2.0) to the repository root.
  • mediumtopics#3
    Correct typo in topics and ensure 'awesome' is present

    Why:

    CURRENT
    awsome, chatgpt, gpt-4, kg, knowledge-graph, language-model, large-language-model, llm, survey
    COPY-PASTE FIX
    awesome, chatgpt, gpt-4, kg, knowledge-graph, language-model, large-language-model, llm, survey

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface RManLuo/Awesome-LLM-KG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 2×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Weaviate · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to integrate structured factual knowledge into large language models effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Weaviate
    4. Pinecone
    5. Neo4j
    6. RDFox
    7. Grakn (now Vaticle's TypeDB)
    8. Hugging Face Transformers
    9. OpenAI API (Fine-tuning endpoint)

    AI recommended 9 alternatives but never named RManLuo/Awesome-LLM-KG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest research trends in combining large language models with knowledge graphs?
    you: not recommended
    AI recommended (in order):
    1. K-BERT
    2. ERNIE (Baidu's version)
    3. LUKE
    4. GraPPa
    5. KG-BERT
    6. OpenIE
    7. GPT-3/4
    8. REBEL (Relation Extraction By End-to-end Language generation)
    9. CoDEx (Contextualized Knowledge Graph Embedding)
    10. RAG (Retrieval Augmented Generation)
    11. Neo4j
    12. Wikidata
    13. KGLM (Knowledge-Grounded Language Model)
    14. KG-FiD (Knowledge Graph Fused-in-Decoder)
    15. Logic-LLM
    16. DeepMind's GATO
    17. Amazon Neptune
    18. Claude 3

    AI recommended 18 alternatives but never named RManLuo/Awesome-LLM-KG. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of RManLuo/Awesome-LLM-KG?
    pass
    AI did not name RManLuo/Awesome-LLM-KG — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts RManLuo/Awesome-LLM-KG in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name RManLuo/Awesome-LLM-KG — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo RManLuo/Awesome-LLM-KG solve, and who is the primary audience?
    pass
    AI did not name RManLuo/Awesome-LLM-KG — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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RManLuo/Awesome-LLM-KG — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite