IvyGraph

A semantic graph-based wordlist generator powered by WordNet, Wikipedia and LLMs.

IvyGraph builds a semantic knowledge graph starting from a small set of keywords, expands it using an AI agent, and generates contextually relevant word combinations applicable to any domain requiring semantic vocabulary expansion, like fuzzing, query expansion, generation of coherent and specific vocabulary, tagging...

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How it works

Given a list of keywords, IvyGraph follows this pipeline:

1. Graph construction Each keyword is placed in a semantic DAG by climbing its hypernym chain via WordNet.

2. LLM fallback If a term is too specific or absent from WordNet, an AI agent fetches a Wikipedia summary and iteratively finds a suitable hypernym until the term can be anchored in the graph.

3. Graph expansion The graph is expanded bottom-up: for each terminal node (a node with leaves), an LLM generates new contextually relevant hyponyms.

4. Similarity scoring Wu-Palmer similarity is computed between all leaf pairs. The most semantically related combinations are ranked and used to generate word variants via synonym expansion and permutation. LIST_OF_WORDS → Graph → Expanded Graph → Wu-Palmer pairs → Synonyms → Wordlist

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Example output

Input:

"football", "messi", "champions league", "tennis", "formula1", "verstappen"

Sample generated pairs: {'words': ['messi', 'verstappen'], 'wp_distance': 0.912} {'words': ['football', 'tennis'], 'wp_distance': 0.876} {'words': ['champions league', 'formula1'], 'wp_distance': 0.843}

Sample wordlist output: Football MessiChampions TennisLeague Verstappen FormulaChampions ...

There is a Proof of Concept in the repo that's shows how, when given ["Cyberpunk2077", "Rabbit"] as input, it completely excludes the combination between the two due to semantic incorrelation (See the poc/ folder), without sloppy outputs that a simple and uncontrolled response to prompt will have. With the graph you potentially can also control how much "expand" the depth of a word (leaf), by adding to it new hyponyms. That would obviously consist in a more complex graph, I'm working on personalizing those specifities.

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Setup

Prerequisites:

Python 3.14.5

1. Clone the repository

git clone https://github.com/ivegotanheadache/Ivygraph.git
cd ivygraph

2. Create and activate a virtual environment

# Linux / macOS
python -m venv venv
source venv/bin/activate

# Windows
python -m venv venv
venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt
python -c "import nltk; nltk.download('wordnet')"

For local LLM support (optional):

pip install llama-cpp-python

4. Configure your API key

Create a .env file in the project root: OPENAI_API_KEY=your_key_here

IvyGraph also supports a local LLM via llama.cpp as an alternative to OpenAI — uncomment the Llama block in the source and set your model path.

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Disclaimer

IvyGraph is intended for educational, research and authorized security testing only. Only use it against systems you own or have explicit permission to test.