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Build Knowledge Graphs with Generative AI and LLMs

Introduction
Extracting Entities and Relationships
Extracting the Person Entity
Extracting the Position and Company Entities
Extracting the Skill Entities
Extracting the Education Entities
Creating Relationships between Entities
Conclusion
FAQs
Additional Resources

Introduction

In this video series, we will be exploring the concept of knowledge graphs. We will start with raw text data and use LLMs (Language Learning Models) and Generative AI models to extract entities and relationships between them. We will then take this extracted data and ingest it into graph databases using Neo4j and the Cypher graph database language. Using Cypher, we will query the databases both in natural language and using predefined queries. Additionally, we will build a few graph ML applications, such as creating node embeddings and graph features for other machine learning use cases, as well as building a chatbot on top of the graph database.

Extracting Entities and Relationships

Before we dive into the details of knowledge graphs, it is important to understand how we extract entities and relationships from raw text data. Extracting information from unstructured data can be a complex task, but by using LLMs, we can train models to identify specific entities and relationships Based on predefined templates.

Extracting the Person Entity

The first entity we will extract is the person entity. This entity represents an individual Mentioned in the raw text data, such as a job applicant. We will look for specific Patterns or keywords to identify the person entity and extract additional information, such as their title, company, and skills. By using Prompts and templates, we can guide the LLM models to provide us with the desired information.

Extracting the Position and Company Entities

Next, we will extract the position and company entities. These entities represent the job positions and the companies the person has worked for. Similar to extracting the person entity, we will use prompts and templates to guide the LLM models in identifying and extracting the desired information. We will also Create a relationship between the person entity and the position and company entities to establish the connection between them.

Extracting the Skill Entities

Another important entity in the knowledge graph is the skill entity. This entity represents the skills possessed by the person. By using prompts and templates, we can extract information about the skills mentioned in the raw text data, such as the skill name and skill level. We will then create relationships between the person entity and the skill entities to indicate the skills possessed by the person.

Extracting the Education Entities

The education entity represents the educational background of the person. We can extract information about the degree, graduation date, and the educational institution attended. Similar to extracting other entities, we will use prompts and templates to guide the LLM models in extracting the desired information. We will create relationships between the person entity and the education entities to indicate the educational background of the person.

Creating Relationships between Entities

After extracting the entities, we will proceed to create relationships between them. These relationships help establish connections and dependencies between the entities in the knowledge graph. We will create relationships such as the person has a position at a company, the person has skills, and the person has education. By creating these relationships, we can effectively represent the connections between different entities in the knowledge graph.

Conclusion

In this video series, we have learned about knowledge graphs and how to extract entities and relationships from raw text data using LLMs. We have explored the extraction of the person, position, company, skill, and education entities, and learned how to create relationships between them. By building a knowledge graph, we can organize and represent complex data in a structured and Meaningful way, enabling us to gain valuable insights and answer complex queries.

FAQs

Q: What is a knowledge graph? A: A knowledge graph is a structured representation of knowledge that uses entities and relationships to organize and store information. It enables data to be connected and queried in a meaningful way, facilitating the discovery of insights and patterns.

Q: How does the extraction of entities and relationships work? A: The extraction process involves using Language Learning Models (LLMs) and prompts to train models to identify specific entities and relationships based on predefined templates. By providing clear instructions and prompts, we can guide the models to extract the desired information.

Q: What are the benefits of using knowledge graphs? A: Knowledge graphs offer several benefits, including improved data organization and structure, enhanced data querying and analysis capabilities, and the ability to uncover hidden insights and patterns. They enable a more holistic understanding of complex data and support various applications such as recommendation systems, information retrieval, and knowledge management.

Q: How can knowledge graphs be used in real-world applications? A: Knowledge graphs have various real-world applications, such as job recruitment, customer relationship management, fraud detection, and medical research. They can be leveraged to organize and analyze large amounts of data, allowing businesses and organizations to make informed decisions and gain valuable insights.

Q: What tools or technologies are commonly used to build and query knowledge graphs? A: There are several tools and technologies for building and querying knowledge graphs. Popular graph database systems like Neo4j and RDF triple stores like Apache Jena are commonly used for storing and managing graph data. Query languages such as Cypher and SPARQL are used to query and manipulate the data in these graph databases.