REPOGEO REPORT · LITE

littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27

Default branch main · commit c87c733b · scanned 6/6/2026, 6:48:01 AM

GitHub: 528 stars · 73 forks

AI VISIBILITY SCORE

17 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 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 littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27, 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 relevant topics to the repository

Why:

COPY-PASTE FIX

knowledge-graph, llm, chatbot, q-a-system, nlp, information-extraction, chatglm, paddlepaddle, pytorch, vuejs

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

(Create a LICENSE file in the repository root with an appropriate open-source license, e.g., MIT or Apache-2.0, to clarify usage terms.)

mediumreadme#3

Clarify the system's end-to-end nature in the README's opening

Why:

CURRENT

Combining Knowledge Graph Construction, Graph Completion, and ChatGLM for Intelligent Q&A

COPY-PASTE FIX

ChatKG is a comprehensive, end-to-end system for building intelligent Q&A solutions, combining knowledge graph construction, graph completion, and large language models like ChatGLM for advanced conversational AI.

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 littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Prodigy

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Prodigy · recommended 2×
neo4j/neo4j · recommended 1×
vaticle/typedb · recommended 1×
Amazon Neptune · recommended 1×
explosion/spaCy · recommended 1×

CATEGORY QUERY
How to build an intelligent Q&A system using knowledge graphs and large language models?
you: not recommended
AI recommended (in order):
1. Neo4j (neo4j/neo4j)
2. TypeDB (vaticle/typedb)
3. Amazon Neptune
4. SpaCy (explosion/spaCy)
5. OpenIE
6. Stanford CoreNLP (stanfordnlp/CoreNLP)
7. Prodigy
8. LangChain (langchain-ai/langchain)
9. LlamaIndex (run-llama/llama_index)
10. Hugging Face Transformers (huggingface/transformers)
11. OpenAI API
12. Azure OpenAI Service
13. Anthropic Claude API
14. Google Gemini API
15. Cypher
16. Gremlin
17. TypeQL
18. SPARQL
19. Pydantic (pydantic/pydantic)
AI recommended 19 alternatives but never named littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help extract knowledge from raw text for a knowledge graph-powered LLM chatbot?
you: not recommended
AI recommended (in order):
1. spaCy
2. OpenNRE
3. Stanza
4. GraphDB
5. Neo4j
6. Haystack
7. Prodigy
AI recommended 7 alternatives but never named littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
fail
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 littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27?
pass
AI did not name littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27 — 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 littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27 in production, what risks or prerequisites should they evaluate first?
pass
AI named littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27 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 littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27 solve, and who is the primary audience?
pass
AI did not name littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27 — 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 littlewwwhite/KnowledgeGraph-based-on-Raw-text-A27. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

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HTML

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