RRepoGEO

REPOGEO REPORT · LITE

rahulnyk/knowledge_graph

Default branch main · commit 28535863 · scanned 5/21/2026, 9:22:52 AM

GitHub: 3,190 stars · 515 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
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 rahulnyk/knowledge_graph, 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
    Add a concise opening sentence to the README

    Why:

    COPY-PASTE FIX
    This repository provides a complete, end-to-end Python pipeline to automatically extract entities and relationships from any unstructured text corpus and construct a knowledge graph.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    knowledge-graph, nlp, information-extraction, graph-augmented-generation, spacy, python, text-to-graph, semantic-network
  • mediumhomepage#3
    Add the GitHub Pages link to the repository homepage field

    Why:

    COPY-PASTE FIX
    https://rahulnyk.github.io/knowledge_graph/

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 rahulnyk/knowledge_graph
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stanford CoreNLP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Stanford CoreNLP · recommended 2×
  2. spaCy · recommended 2×
  3. OpenNRE · recommended 1×
  4. GraphDB · recommended 1×
  5. Stardog · recommended 1×
  • CATEGORY QUERY
    How to automatically build a knowledge graph from unstructured text documents?
    you: not recommended
    AI recommended (in order):
    1. OpenNRE
    2. Stanford CoreNLP
    3. spaCy
    4. GraphDB
    5. Stardog
    6. Apache Jena
    7. Haystack

    AI recommended 7 alternatives but never named rahulnyk/knowledge_graph. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best libraries for extracting entities and relations to create a knowledge graph?
    you: not recommended
    AI recommended (in order):
    1. spaCy
    2. Hugging Face Transformers
    3. Stanford CoreNLP
    4. AllenNLP
    5. NLTK
    6. OpenIE

    AI recommended 6 alternatives but never named rahulnyk/knowledge_graph. 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 rahulnyk/knowledge_graph?
    pass
    AI named rahulnyk/knowledge_graph explicitly

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

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

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

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rahulnyk/knowledge_graph — 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