This repository contains a project on Movies and Series Graph and Knowledge Inferencing using knowledge graphs. The project aims to create a knowledge graph representation of movies and series data and perform inferencing to derive useful insights and recommendations.
The project focuses on building a knowledge graph that represents relationships between movies, series, actors, directors, genres, and other relevant entities. By analyzing the graph structure and leveraging the relationships, the project aims to provide insights into the data, such as recommendations based on user preferences, actor collaborations, genre trends, and more.
Creation of a knowledge graph: The project involves the extraction of data from various sources, such as IMDb, Wikipedia, or custom datasets, to build a comprehensive knowledge graph representing movies and series. Entity relationships: The graph captures relationships between entities, including movies, series, actors, directors, genres, and more. These relationships enable inferencing and analysis.
By traversing the graph and analyzing the relationships, the project performs inferencing to derive meaningful insights. This includes recommending similar movies, identifying actor collaborations, determining popular genres, and other relevant inferences. User interaction: The project may provide a user interface or command-line interface for users to interact with the knowledge graph, input preferences, and receive personalized recommendations.
To run the project, you need:
Python 3.x or later. Knowledge of graph databases or graph processing frameworks (e.g., Neo4j, RDFLib, NetworkX). Data extraction and parsing skills. Understanding of data structures and algorithms for graph traversal and inferencing.
Clone the repository:
Install the required dependencies mentioned in the project documentation.
Prepare the data: Depending on the chosen data source, you may need to download or collect movie and series data in a suitable format (e.g., CSV, JSON, RDF).
Import the data into the knowledge graph: Use the provided scripts or modules to populate the graph database or construct the graph in memory.
Execute the main script or launch the user interface to interact with the knowledge graph and perform inferencing tasks.
Explore the inferencing results: The project will generate insights, recommendations, or other relevant information based on the inferencing algorithms and queries implemented.