RRepoGEO

REPOGEO REPORT · LITE

LIANGKE23/Awesome-Knowledge-Graph-Reasoning

Default branch main · commit 7d409a6c · scanned 5/28/2026, 7:04:07 PM

GitHub: 1,452 stars · 157 forks

AI VISIBILITY SCORE
22 /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
1 / 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 LIANGKE23/Awesome-Knowledge-Graph-Reasoning, 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
  • highreadme#1
    Reposition README opening to emphasize 'awesome list' nature

    Why:

    CURRENT
    AKGR is a collection of knowledge graph reasoning works, including papers, codes and datasets :fire:.
    COPY-PASTE FIX
    AKGR is an **awesome list** and **curated collection** of key knowledge graph reasoning works, including papers, codes, and datasets, designed for researchers and practitioners.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the content of the MIT License. This is a common choice for open-source content collections.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL in the 'About' section to `https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning`.

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 LIANGKE23/Awesome-Knowledge-Graph-Reasoning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Geometric (PyG)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Geometric (PyG) · recommended 1×
  2. DGL (Deep Graph Library) · recommended 1×
  3. OpenKE · recommended 1×
  4. AmpliGraph · recommended 1×
  5. LibKGE · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources for advanced knowledge graph reasoning techniques and datasets?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG)
    2. DGL (Deep Graph Library)
    3. OpenKE
    4. AmpliGraph
    5. LibKGE
    6. RDFox
    7. TypeDB
    8. YAGO
    9. DBpedia
    10. Wikidata

    AI recommended 10 alternatives but never named LIANGKE23/Awesome-Knowledge-Graph-Reasoning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best research papers and code for inductive and temporal knowledge graph reasoning?
    you: not recommended
    AI recommended (in order):
    1. RE-NET
    2. PyTorch
    3. TANGO
    4. TensorFlow
    5. Know-Evolve
    6. Theano
    7. xERTE
    8. DyGIE
    9. TeRo

    AI recommended 9 alternatives but never named LIANGKE23/Awesome-Knowledge-Graph-Reasoning. 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 LIANGKE23/Awesome-Knowledge-Graph-Reasoning?
    pass
    AI did not name LIANGKE23/Awesome-Knowledge-Graph-Reasoning — 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 LIANGKE23/Awesome-Knowledge-Graph-Reasoning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LIANGKE23/Awesome-Knowledge-Graph-Reasoning explicitly

    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 LIANGKE23/Awesome-Knowledge-Graph-Reasoning solve, and who is the primary audience?
    pass
    AI did not name LIANGKE23/Awesome-Knowledge-Graph-Reasoning — 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?

Embed your GEO score

Drop this badge into the README of LIANGKE23/Awesome-Knowledge-Graph-Reasoning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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LIANGKE23/Awesome-Knowledge-Graph-Reasoning — 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