Leveraging Knowledge Graphs for Agentic Workflows: A Practical Approach to Data Modeling and Querying

Unlocking the power of knowledge graphs: how integrating multiple data sources, including unstructured and structured data, enables advanced analytics, precision, and explainability in AI-driven workflows.

1. Topic of the discussion: Using a graph architecture to deal with multiple data sources, both structured and unstructured, in music analysis.
2. A general graph architecture consists of agents, tools, and a knowledge graph in the middle.
3. The knowledge graph can extract data from documents and unstructured places, as well as handle standard ETLs for structured data.
4. The purpose of having a knowledge graph is to improve accuracy, explanability, and agentic workflows.
5. With a knowledge graph, agents can decompose complex questions into multiple queries and retrieve the necessary data more effectively.
6. A simple data model can be expressed to help agents perform decomposition and pull information accurately.
7. Additional data can be added to the knowledge graph over time to expand its capabilities.
8. An example of a use case for this type of architecture is an employee graph for skills analysis, team collaboration, and identifying skill gaps.
9. The employee graph can start with data from resumes in PDF format, containing professional experience and descriptions.
10. Documents are loaded into the Neo4j graph database, where they are embedded and linked together.
11. An agent is created using Google's ADK framework to interact with the knowledge graph and answer questions.
12. The initial version of the agent may only be able to perform simple search queries, which might not provide accurate or sufficient answers for complex questions.
13. To improve the data model, entity extraction can be performed on the documents to create a more expressive graph with connections between people, skills, and activities.
14. The refined data model allows for more precise questions and aggregations, providing better answers for queries about technical talent distribution or finding similarities between individuals base
15. Additional tools can be added to the agent to enhance its capabilities, such as using language models to generate cipher text or performing complex traversals in a graph database.
16. With a more expressive data model and enhanced tools, questions can be answered with greater precision and control, allowing for better filtering, refinement, and explanations of the results.
17. As new data sources become available, they can be easily added to the graph without requiring significant changes to the existing data model or schema.
18. For example, internal HR data about projects and collaborations can be integrated into the graph, allowing for questions about teamwork and project-specific contributions.
19. Graph databases enable flexible integration of new relationships and data types, which is particularly useful when working with agents that need to quickly adapt to changing requirements and incor
20. By using a knowledge graph architecture, users can improve the accuracy, performance, and flexibility of their music analysis systems when dealing with multiple data sources.

Source: AI Engineer via YouTube

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