Unlocking the Power of Knowledge Graphs: RAI Demo Highlights

Unlocking the Power of Knowledge Graphs: RAI Demo Highlights

Table of Contents:

1. Introduction
2. Relational AI and the Vision for the Product
3. Data Applications Built on Knowledge Graphs
4. The Knowledge Graph Demo: Solving a Business Problem
5. Understanding Knowledge Graphs: From Imperative Programming to Declarative Language Delve
6. Overview of the Reference Architecture for Data Apps
7. The Role of Jupiter Notebook in the Demo
8. The Business Problem: Choosing Relevant Learning Materials for New Sales Hires
9. Building the Knowledge Graph: Core Nodes and Relationships
10. Ingesting and Modeling the Business Problem with Lemma CSV
11. Defining Relations for Document and Concept Nodes
12. The "About" Relationships and Weights in the Knowledge Graph
13. Querying the Knowledge Graph: Using Regular Expression to Find Documents and About Weights
14. Computed Knowledge and Runtime Queries in the Knowledge Graph
15. Adding Computed Attributes to the Knowledge Graph
16. Suggestions Based on Document Content: Computing Focus for Each Document on Concepts
17. Creating the "Suggested" Relation for a Top-End List of Documents
18. Joining Multiple Concepts: Querying for Documents about Both Topics
19. Incorporating Reviewer Knowledge in the Knowledge Graph
20. The Dynamic Role Attribute for Reviewers: Learner, Employee, or Curator
21. Data Ingestion, ELT, and Integrity Constraints in the Full Demo
22. Reviewer Roles and Document Tracking in Google Sheets
23. Ben and Steve: Reviewers with Different Onboarding Purposes
24. Reconciling Different Formats in Reviewer Tracking Sheets
25. Data Preparation: Brief Views and Detailed Views for Input CSVs
26. Cleaning Up Reviewer Data: Eliminating Unwanted Entries
27. Matching Names and Finding Intersections in Reviewer Lists
28. Next Steps and Conclusion

Introduction

In this article, we will explore the concept of data applications built on knowledge graphs, focusing on a demo that demonstrates how a knowledge graph can solve a business problem. We will dive into the Journey from imperative programming to a declarative language called Delve, and understand the reference architecture for data apps built on Relational AI's platform. Additionally, we will discuss the role of Jupiter Notebook in the demo and the specific business problem this knowledge graph addresses. Join us as we explore the intricacies of building a knowledge graph, querying the graph for relevant information, and incorporating reviewer knowledge to enhance the graph's capabilities.

Relational AI and the Vision for the Product

Relational AI is a company dedicated to building a relational knowledge graph management system. With a vision for revolutionizing data applications, their product aims to leverage the power of knowledge graphs to unlock new possibilities in solving business problems. By connecting and analyzing data in a relational manner, Relational AI envisions a future where businesses can seamlessly navigate complex information landscapes and make data-driven decisions with ease. To delve deeper into their vision, visit their Website or explore other videos discussing it.

Data Applications Built on Knowledge Graphs

Data applications built on knowledge graphs offer a unique approach to problem-solving. By structuring data in a graph-like format, relationships between different entities become explicit and can be leveraged to derive Meaningful insights. These applications go beyond traditional database-centric approaches by enabling data-centric business modeling. This shift in perspective allows for more flexible and efficient solutions to complex problems. In the following sections, we will explore a demo that showcases the power of data applications built on knowledge graphs and how they can revolutionize various industries.

The Knowledge Graph Demo: Solving a Business Problem

The demo we will be focusing on involves using a knowledge graph to solve a specific business problem. In this case, the problem revolves around the onboarding process for new sales or sales-engineering hires. The goal is to choose the most relevant learning materials to bring these new hires up to speed quickly and effectively. By using a knowledge graph, we can suggest learning materials based on their conceptual content, quality, degree of difficulty, and appropriateness for a sequence learning plan. In the following sections, we will walk through the process of building the knowledge graph and how it addresses this business problem.

Understanding Knowledge Graphs: From Imperative Programming to Declarative Language Delve

Before diving into the demo, it is essential to understand the transition from imperative programming languages like Java, C++, and Python to Relational AI's declarative language called Delve. Many years were spent using legacy languages and SQL in a database-oriented approach to solving business problems. However, with the emergence of knowledge graphs and Delve, there is a paradigm shift towards a data-centric business modeling approach. Delve enables the creation of powerful and efficient solutions by allowing developers to focus on the relationships and connections within the data rather than the implementation details of the underlying programming languages. This shift opens up a whole new world of possibilities, which we will explore throughout this article.

Overview of the Reference Architecture for Data Apps

To better understand the demo and its components, it's important to grasp the reference architecture for data apps built on Relational AI's platform. The architecture Diagram showcases how different elements come together to Create data-driven solutions. In the full demo, each component plays a vital role, but for the purpose of this article, we will skip ahead to the core of the demo itself. However, by familiarizing ourselves with the reference architecture, we can gain a comprehensive understanding of how the knowledge graph fits into the larger picture.

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