RRepoGEO

REPOGEO REPORT · LITE

HKUST-KnowComp/AutoSchemaKG

Default branch main · commit d0a1666a · scanned 6/1/2026, 9:57:57 AM

GitHub: 751 stars · 101 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 HKUST-KnowComp/AutoSchemaKG, 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
  • hightopics#1
    Add more specific topics to improve categorization

    Why:

    CURRENT
    graph-construction, knowledge-graph, rag
    COPY-PASTE FIX
    graph-construction, knowledge-graph, rag, schema-generation, schema-induction, llm-applications, knowledge-extraction
  • mediumreadme#2
    Add a 'Key Features' section to highlight unique aspects

    Why:

    COPY-PASTE FIX
    Add a 'Key Features' section early in the README, including bullet points like:
- **Automatic Schema Generation:** Induce knowledge graph schemas (entity types, relation types) directly from unstructured text.
- **LLM-Powered Triple Extraction:** Extract entities and events using large language models.
- **High-Quality KG Construction:** Build robust knowledge graphs without requiring predefined schemas.
- **RAG Application Enhancement:** Specifically designed to improve retrieval-augmented generation systems.
  • lowreadme#3
    Add a comparison section to explicitly state differentiators

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why AutoSchemaKG? Differentiating Features' or 'Comparison with Alternatives' that explicitly contrasts AutoSchemaKG's automatic schema generation with tools that require manual schemas or focus solely on general NLP tasks.

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 HKUST-KnowComp/AutoSchemaKG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
thunlp/OpenNRE
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. thunlp/OpenNRE · recommended 1×
  2. Stanford CoreNLP · recommended 1×
  3. explosion/spaCy · recommended 1×
  4. deepset-ai/haystack · recommended 1×
  5. usc-isi-i2/kgtk · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for automated knowledge graph construction from text?
    you: not recommended
    AI recommended (in order):
    1. OpenNRE (thunlp/OpenNRE)
    2. Stanford CoreNLP
    3. spaCy (explosion/spaCy)
    4. Haystack (deepset-ai/haystack)
    5. KGTK (usc-isi-i2/kgtk)
    6. GraphFlow
    7. RelationFactory

    AI recommended 7 alternatives but never named HKUST-KnowComp/AutoSchemaKG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build a knowledge graph from text to enhance RAG applications?
    you: not recommended
    AI recommended (in order):
    1. Neo4j AuraDS
    2. Graph Data Science Library (GDS)
    3. SpaCy
    4. NetworkX
    5. Stardog
    6. Amazon Neptune
    7. Amazon Comprehend
    8. Google Cloud Natural Language API
    9. OpenNRE

    AI recommended 9 alternatives but never named HKUST-KnowComp/AutoSchemaKG. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 HKUST-KnowComp/AutoSchemaKG?
    pass
    AI named HKUST-KnowComp/AutoSchemaKG explicitly

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

  • If a team adopts HKUST-KnowComp/AutoSchemaKG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HKUST-KnowComp/AutoSchemaKG 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 HKUST-KnowComp/AutoSchemaKG solve, and who is the primary audience?
    pass
    AI named HKUST-KnowComp/AutoSchemaKG explicitly

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

Embed your GEO score

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HKUST-KnowComp/AutoSchemaKG — 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