Graph ML: Build Knowledge Graphs using Generative AI and LLMs

Introduction

In this article, we will explore how to build knowledge graphs using generative AI and large language models (LLMs). Knowledge graphs are a powerful way to represent and organize complex information by capturing relationships between entities. By extracting entities and relationships from raw text using LLMs, we can create a structured knowledge graph that can be ingested into a graph database such as Neo4j. We will also learn how to query the graph database using a graph query language called Cipher and build graph machine learning applications using tools like LINE and Grad-CI. So let's dive in and explore the exciting world of knowledge graphs!

Extracting Entities and Relationships

The first step in building a knowledge graph is to extract entities and relationships from raw text. In our case, we will be working with resumes as our data source. We will use LLMs, such as those available in Google's Vertex AI, to extract entities like person, position, company, education, and skills from the raw text. We will create specific prompts for each entity using a predefined schema, and the LLMs will generate responses that adhere to this schema. We will carefully design the prompts to ensure accurate extraction of entities and avoid hallucination or imputing missing values.

Ingesting Data into Graph Databases

Once we have extracted the entities and relationships from the raw text, we will ingest the data into a graph database. In this tutorial, we will be using Neo4j as our graph database. We will learn how to create the schema for our graph database, define the appropriate labels, properties, and relationships, and import the extracted data into the database. We will also explore the Cipher graph query language, which is similar to SQL for relational databases, to query the graph database and retrieve meaningful information from our knowledge graph.

Building Graph ML Applications

In addition to querying the graph database, we can also build graph machine learning applications on top of our knowledge graph. We will learn how to create node embeddings to find similar nodes based on their properties, such as skills or education. We will also explore how to create graph features that can be used in classical machine learning use cases. Furthermore, we will utilize LINE and Grad-CI tools to build applications like chatbots on top of our graph database. With these applications, we can interact with the graph database using natural language questions and receive results in a conversational manner.

Conclusion

In this article, we have explored the process of building knowledge graphs using generative AI and LLMs. We have learned how to extract entities and relationships from raw text using LLMs, ingest the data into a graph database, and build graph machine learning applications. By leveraging the power of knowledge graphs, we can gain valuable insights from unstructured data and make informed decisions. So get ready to dive into the world of knowledge graphs and unlock the potential of your data!

Keywords: Knowledge graphs, Generative AI, LLMs, Graph databases, Neo4j, Cipher graph query language, Graph machine learning, Node embeddings, Graph features, LINE, Grad-CI, Chatbots.

FAQ

Q1: What is a knowledge graph? A knowledge graph is a powerful way to represent and organize complex information by capturing relationships between entities. It provides a structured and graph-based representation of data, enabling efficient querying and analysis.

Q2: How do LLMs help in extracting entities and relationships? LLMs (large language models) use advanced natural language processing techniques to generate responses that adhere to a predefined schema. By designing prompts and instructions, LLMs can extract entities and relationships accurately from unstructured text data.

Q3: What is Cipher graph query language? Cipher is a graph query language specifically designed for Neo4j graph databases. It is similar to SQL for relational databases and allows users to query and manipulate data stored in the graph database.

Q4: Can knowledge graphs be used for machine learning applications? Yes, knowledge graphs can be utilized for building graph machine learning applications. By creating node embeddings and graph features, we can perform similarity-based searches and use them as features in classical machine learning models.

Q5: How do chatbots work on top of graph databases? Chatbots on top of graph databases utilize natural language processing and generation techniques to convert user queries into graph queries. These queries are processed by the graph database, and the chatbot provides responses based on the retrieved information from the knowledge graph.

Q6: What are some common use cases for knowledge graphs? Some common use cases for knowledge graphs include recommendation systems, fraud detection, semantic search, personalized medicine, and knowledge management systems.