RRepoGEO

REPOGEO REPORT · LITE

DataArcTech/ToG

Default branch main · commit 7ccbb92e · scanned 6/16/2026, 11:28:08 PM

GitHub: 650 stars · 74 forks

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 DataArcTech/ToG, 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 the README's opening to clearly state the project's purpose for LLM reasoning.

    Why:

    CURRENT
    # ToG
The code for paper: "Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph".
    COPY-PASTE FIX
    # ToG: Deep and Responsible LLM Reasoning on Knowledge Graphs
This repository provides the official code for "Think-on-Graph (ToG)", our ICLR 2024 paper, which introduces a novel framework for enhancing Large Language Model (LLM) reasoning capabilities by leveraging structured knowledge graphs to improve factuality and reduce hallucinations.
  • highlicense#2
    Add a LICENSE file to the repository.

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a suitable open-source license (e.g., MIT, Apache-2.0, or GPL-3.0) that reflects your intended usage and contribution model.
  • mediumabout#3
    Update the repository description and add a homepage link.

    Why:

    CURRENT
    Description: This is the official github repo of Think-on-Graph (ICLR 2024). If you are interested in our work or willing to join our research team in Shenzhen, please feel free to contact us by email (xuchengjin@idea.edu.cn)
Homepage: (none)
    COPY-PASTE FIX
    Description: Official code for Think-on-Graph (ToG), an ICLR 2024 framework that enhances Large Language Model (LLM) reasoning by integrating structured knowledge graphs for improved factuality and reduced hallucinations.
Homepage: [Link to paper on arXiv/project page/ICLR proceedings]

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 DataArcTech/ToG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 1×
  2. RDFox · recommended 1×
  3. TypeDB · recommended 1×
  4. Amazon Neptune · recommended 1×
  5. Stardog · recommended 1×
  • CATEGORY QUERY
    How to improve large language model reasoning capabilities using structured knowledge graphs?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. RDFox
    3. TypeDB
    4. Amazon Neptune
    5. Stardog
    6. DGL
    7. PyTorch Geometric
    8. Wikidata

    AI recommended 8 alternatives but never named DataArcTech/ToG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for enhancing LLM factuality and reducing hallucinations with external knowledge.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. RAGatouille
    5. DSPy
    6. Microsoft Guidance

    AI recommended 6 alternatives but never named DataArcTech/ToG. 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 DataArcTech/ToG?
    pass
    AI named DataArcTech/ToG explicitly

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

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

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

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DataArcTech/ToG — 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