RRepoGEO

REPOGEO REPORT · LITE

lihanghang/NLP-Knowledge-Graph

Default branch master · commit c499e384 · scanned 5/21/2026, 1:12:53 AM

GitHub: 1,760 stars · 371 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
28 /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
2 / 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 lihanghang/NLP-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
    Reposition README H1 and opening paragraph to clarify repo's nature

    Why:

    CURRENT
    # Deep learning for Knowledge-Graph

> create time 2019-08-24

> 探索认知智能系列趋势：1. 数据融合知识；2. All in LLM。

> 包括知识获取、知识库构建、基于知识库的问答系统系列技术研究与应用。涉及到NLP领域的前沿技术和论文。
    COPY-PASTE FIX
    # NLP-Knowledge-Graph: A Curated Research Hub for Deep Learning, LLMs, and Dialogue Systems

> This repository serves as a comprehensive, curated collection of research papers, tools, datasets, and summaries focused on Natural Language Processing (NLP), Knowledge Graphs, Dialogue Systems, and Large Language Models (LLMs). It aims to explore cognitive intelligence trends, from knowledge acquisition and knowledge base construction to knowledge-based question answering systems.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Your personal blog, project page, or relevant external resource URL]
  • lowreadme#3
    Add a dedicated 'What is this repo?' section to the README

    Why:

    CURRENT
    The README immediately lists a table of contents after the H1.
    COPY-PASTE FIX
    ## What is NLP-Knowledge-Graph?

This repository is designed as a living compendium for researchers, students, and practitioners interested in the intersection of Natural Language Processing (NLP) and Knowledge Graphs. It aggregates cutting-edge research, practical tools, and relevant datasets, providing structured summaries and analyses across key areas like knowledge acquisition, KBQA, dialogue systems, and the application of large language models. Our goal is to track and synthesize the evolving landscape of cognitive 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 lihanghang/NLP-Knowledge-Graph
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. explosion/spaCy · recommended 2×
  3. elastic/elasticsearch · recommended 2×
  4. weaviate/weaviate · recommended 2×
  5. OpenAI GPT-4 / GPT-3.5 Turbo · recommended 1×
  • CATEGORY QUERY
    How to build a knowledge graph using deep learning and large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo
    2. Hugging Face Transformers (huggingface/transformers)
    3. SpaCy (explosion/spaCy)
    4. Dedupe (dedupeio/dedupe)
    5. Neo4j
    6. Amazon Neptune
    7. RDFox
    8. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
    9. DGL (Deep Graph Library) (dmlc/dgl)

    AI recommended 9 alternatives but never named lihanghang/NLP-Knowledge-Graph. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for developing knowledge-based question answering systems?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset-ai/haystack)
    2. Elasticsearch (elastic/elasticsearch)
    3. FAISS (facebookresearch/faiss)
    4. Weaviate (weaviate/weaviate)
    5. Rasa (RasaHQ/rasa)
    6. LangChain (langchain-ai/langchain)
    7. OpenAI API
    8. Pinecone
    9. Weaviate (weaviate/weaviate)
    10. Qdrant (qdrant/qdrant)
    11. Elasticsearch (elastic/elasticsearch)
    12. SpaCy (explosion/spaCy)
    13. Hugging Face Transformers (huggingface/transformers)
    14. Apache Lucene (apache/lucene)
    15. Solr (apache/solr)

    AI recommended 15 alternatives but never named lihanghang/NLP-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 lihanghang/NLP-Knowledge-Graph?
    pass
    AI did not name lihanghang/NLP-Knowledge-Graph — 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 lihanghang/NLP-Knowledge-Graph in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lihanghang/NLP-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 lihanghang/NLP-Knowledge-Graph solve, and who is the primary audience?
    pass
    AI named lihanghang/NLP-Knowledge-Graph explicitly

    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 lihanghang/NLP-Knowledge-Graph. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/lihanghang/NLP-Knowledge-Graph.svg)](https://repogeo.com/en/r/lihanghang/NLP-Knowledge-Graph)
HTML
<a href="https://repogeo.com/en/r/lihanghang/NLP-Knowledge-Graph"><img src="https://repogeo.com/badge/lihanghang/NLP-Knowledge-Graph.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

lihanghang/NLP-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