Convert Text into Knowledge Graph Easily

In this video, the host from Hands-on AI demonstrates how to convert any text into a knowledge graph. The knowledge graph consists of nodes (entities) and the relationships between them. The video uses a sports commentary document from an AFL match as an example, but the method can be applied to various types of documents, including text files, PDFs, HTML files, and even audio or video files.

The process begins by loading documents from a directory using LangChain’s directory loader, which handles multiple document types. The documents are then split into smaller chunks to manage large corpora of text effectively. Each chunk is processed using a large language model (LLM) to extract entities and relationships. The LLM is prompted to identify key terms and their relationships, which are then formatted into a JSON structure.

The extracted data is organized into a dataframe for convenience, tracking each chunk and its source document. The video explains how to handle direct relationships between nodes and introduces the concept of contextual proximity, which establishes relationships between nodes appearing in the same chunk even if they are not directly related.

The extracted relationships are aggregated, and the nodes and relationships are visualized using the NetworkX library. The video also covers how to detect communities within the knowledge graph using the Louvain method, which groups nodes based on their connections. Each community is assigned a unique color to visualize the graph more effectively.

The host emphasizes the flexibility of the method, allowing users to start with an open-ended approach to understand the data and then refine the knowledge graph by defining explicit data models. The video concludes with a demonstration of using open-source LLMs with the LangChain framework, highlighting the ability to run LLMs locally.

Overall, the video provides a comprehensive guide to converting text into a knowledge graph, making it accessible for various applications in data science, machine learning, and artificial intelligence.