As the rise of LLMs has pushed the boundaries of what AI models can understand and generate, it has also showcased some of their detrimental limitations: hallucinations, lack of contextual understanding, and an overall absence of grounded knowledge needed for reliable answers. All of these limitations underscore the need for more sophisticated data management and retrieval systems.

Enter Knowledge Graphs (KGs): a more flexible way to represent and store information. By enabling a more nuanced representation of complex, interconnected data, KGs don’t just address the lack of contextual understanding and grounding in the reality of current LLMs. By providing a stable framework for inference and reasoning, they are poised to unlock the potential for the next wave of AI development and the march towards Artificial General Intelligence (AGI).

The SingularityNET team has been at the forefront of advancing Knowledge Graphs for over three decades and is perfectly positioned to pioneer the integration of these sophisticated data models and lay the foundations for a new generation of AI systems.

Why Do We Need Knowledge Graphs?

Imagine you have 1,000 books you want to store on a bookshelf.

Your task is to find the best way to organize them. But naturally, this task has no obvious solution unless you know about the purpose of this book collection.

If you are making a library, you could organize them by their subject.

In a bookstore, however, organizing books by their author, editor, or category may be more useful.

You may want to have a beautiful reading room or corridor in your house, and you organize them by size, color, etc.

Or just by your personal preference.

The conclusion? There’s never an obvious best way to organize information unless you consider the way you expect to use that information.

The analogy of organizing books on a bookshelf aims to highlight the nuances of comparing knowledge graphs and relational databases. Just as the optimal bookshelf arrangement depends on the purpose of the book collection, selecting the right data management system hinges on the specific data requirements and usage patterns.

What is a Knowledge Graph?

Knowledge graphs are a powerful representation of interconnected information that mirrors the complex relationships and semantics present in the real world.

They’re made up of nodes and edges.

The nodes represent different entities, and the edges convey information about how entities relate among themselves.

They are structured representations of information that model relationships between entities. They serve as powerful tools for organizing and retrieving knowledge, and they are increasingly being used in a variety of applications. Like a library’s subject-based organization, knowledge graphs excel at representing complex relationships between entities, making them ideal for applications that require deep understanding and inference.

What is a Relational Database?

At the heart of traditional data storage, relational databases offer a structured, secure approach to data management. Predefined relationships and unique identifiers for each data point ensure organized, accessible data storage.

Relational databases resemble a bookstore’s organizational structure based on attributes such as editor, author, and genre. They excel at handling structured data with well-defined relationships, ensuring data integrity and efficient query performance.

Key Differences Between Knowledge Graphs and Relational Databases

Relational databases index by properties. Properties are like columns in a table. Relational databases build indexes that allow you to have fast access to all elements that satisfy a given condition on the value of a given property. Knowledge graphs are indexed by entities, which are like rows in a table. So, knowledge bases allow you to go from one entity to another based on their shared property values.

Let’s say our task was to build a knowledge base with information about books. Let’s take the properties of a book’s name, genre, and author.

If we were to represent our newly-organized bookshelf in a relational database format, here’s how it would look like:

If we were to represent it in a knowledge graph format, it would look like this:

In this scenario, we are only representing three books, but there may be hundreds, thousands, or even millions.

The relational database is a simple table. Although visually more complex, the knowledge graph also showcases the relationships between different books and their properties (genre/author).

In this example, we represent nodes in round squares and edges in circles. Lines connect edges to nodes to indicate that the node is linked to an edge.

We showcase only two types of edges — “Is A” and “Exec.”

“Is A” indicates inheritance (e.g., “Book 1” is a “Book”, therefore, it inherits all properties of “Book”.

“Property” represents a specific quality of a given node (or book). For example, Book 1 has a property “Genre” whose value is “Thriller.”

The relational representation is great at answering questions like “What are the books under the Sci-Fi genre?” because it may have indexes for fast lookups for the value of a column.

The knowledge graph is great for answering questions like “Give me other books with the same genre as Book 1”.

Writing AI/AGI algorithms that work on relational data is possible, but writing them by traversing a knowledge graph is much more natural. If your algorithm reaches a concept (e.g., “Thrillers” on your bookshelf), it can reason about other concepts related to it just by looking at the neighbor concepts in the graph (the indexes are made to perform this type of operation efficiently).

In essence, knowledge graphs shine when it comes to navigating the intricate web of connections between entities, while relational databases excel in managing structured data with defined relationships.

Here’s a summary of the key differences between knowledge graphs and relational databases:

· Organization of Information: Both knowledge graphs and relational databases can store the same information, but they organize it differently.

· Indexing Differences: The nature of indexing differs between the two systems. Relational databases typically focus on indexing properties of a collection of elements (columns), while knowledge graphs index the elements themselves (rows). This difference in indexing makes knowledge graphs particularly useful for AI/AGI algorithms. This is because KGs index entities (such as people or books), not just their properties. This structure resembles a network where we can explore relationships and reason more naturally, unlocking deeper contextual understanding.

· Representation of Data: In a relational database, data is represented in tables, while in a knowledge graph, it’s represented by nodes and links. Nodes are akin to items in a database, and links are somewhat similar to connectors in a database but are used differently.

· Use in AI Algorithms: Knowledge graphs are organized in a way that is more compatible with AI algorithms, especially Artificial General Intelligence algorithms. They allow these algorithms to run more efficiently than when data is stored in a relational database.

The choice between these two data management tools ultimately boils down to the specific needs of the application. Just as a well-organized bookshelf enhances the accessibility and utilization of a book collection, the judicious selection of a data management system optimizes the storage, retrieval, and analysis of information, empowering applications to achieve their intended goals.

Why Knowledge Graphs Provide a Better Avenue for AGI Development

Thinking back to our bookshelf analogy, a knowledge graph would allow us to organize books that are not just arranged in linear order but also have threads connecting related books, regardless of their physical position.

This structure is particularly beneficial for Artificial General Intelligence, as it mirrors human cognitive processes, facilitating complex tasks like reasoning, understanding context, and learning from unstructured data.

In contrast, a relational database would look more like a traditional library catalog, organizing data in a structured, tabular format. Although efficient for tasks that require us to retrieve specific pieces of information quickly, it might lack the nuanced understanding of relationships and context required for AGI development.

This makes knowledge graphs a cornerstone in the quest for AGI, paving the path to its ultimate realization. Knowledge graphs enable AI to understand and interconnect data in a way that mirrors human context and relationships, providing AI with the essential skill of being grounded in reality and overcoming many of the limitations of current LLM models. This skill is a pivotal leap toward achieving a level of artificial intelligence that rivals human cognition, potentially marking the dawn of genuine artificial logic and reasoning.

For over three decades, SingularityNET scientists have been at the forefront of research and development in knowledge graphs, recognizing their potential for unlocking the true power of artificial intelligence.

Our Distributed AtomSpace (DAS) is a cutting-edge form of dynamic metagraph that serves as a fast and scalable knowledge base. It reflects these decades of work and dedication, and it’s designed to meet the demands of AGI and advanced knowledge graph applications.