This project builds and dynamically updates a knowledge graph from Wikidata, incorporating information from news articles provided by The New York Times API. The project utilizes Neo4j as the graph database.
- Knowledge Graph Construction: Builds a knowledge graph from Wikidata, starting with a list of seed companies and expanding outwards based on specified relationships and depth.
- Dynamic Updates: Continuously updates the knowledge graph with new information extracted from news articles.
- Configurable: Allows customization of the knowledge graph structure, including entity types, relationship depth, and date range.
- Efficient Caching: Implements a caching mechanism to reduce redundant Wikidata queries.
- Demo Graph: Provides an option to build a smaller demo graph for testing and experimentation.
- Python 3.7+
- Neo4j (including a running Neo4j instance)
- A selection of Libraries (install via
pip install -r requirements.txt):- neo4j~=5.0
- colorama *configparser *google-generativeai *requests
- Clone the repository:
git clone <repository_url> - Navigate to the project directory:
cd <project_directory> - Install the required libraries:
pip install -r requirements.txt - Configure the Neo4j connection: Update the
config.inifile with your Neo4j URI, username, and password. An exampleconfig.inifile is provided.
The project is configured using the config.ini file:
[neo4j]
uri = neo4j://localhost:7687
username = neo4j
password = your neo4j password
[gemini]
api_key = your Gemini api key
[nytimes]
api_key = your NYTimes api key
