Knowledge Graphs for AI: Your AI Needs More Than Data

Humans do not just store information in their brains, but they also understand and create checkpoints for everything to make sense and the context of that information. The same is with AI, it not only stores information, but it stores that information in a specific and structured form, so it can be easy for AI to understand the relationships between the entities in that data. This structured representation of connected information is called a knowledge graph for AI.

Let’s understand it better and how it works. We will also look at other aspects of it, such as use cases and more. 

What is a Knowledge Graph?

A knowledge graph is a semantic network of real-world entities, including things, people, scenarios, or concepts, to identify them and define the relationships between them. 

The term was widely popularized in 2012 when Google officially introduced the Google Knowledge Graph as a refinement to their search engine. Because the information is generally stored in a graphical format in the database and is also visualized as a graph structure, it is known as a knowledge graph.’ 

How do knowledge graphs work?

Let’s understand this with a simple example: for instance, AI reads a sentence that says ‘Hazy is a CA at IMB.’ Now, the knowledge graph does not simply copy and store this information as it is a sentence. Knowledge graph sees it as:

• Entities are Hazy & CA (chartered accountant)
• Relationship is ‘works as/ is a.’
• The company is IBM

Hazy — is a — chartered accountant

Hazy — works at — IBM

The entities are called nodes, and the strings connecting them are called edges. 

AI very well understands natural language, but it does not automatically understand the exact structure, context, or real-world relations the way humans do. It is only possible when the information is clearly represented or learned in patterns. And knowledge graphs make it possible, so AI can easily follow connections. 

Core Components of a Knowledge Graph

There are a few key components in the working of a knowledge graph:

1. Nodes: These are the base units of knowledge graphs, such as a person, company, product, situation, or concept. Each node can have different properties that give more details about each entity. 
2. Edges: These are the relationships or connections between nodes. For instance, if Alex likes dark chocolates. Edges define how the dark chocolate and Alex are related to each other. 
3. Properties/Labels: Nodes and edges have various traits and features that tell a lot about them and the type of relationship. An object can have things like name, color, temperature, texture, and more, while an edge will have labels such as ‘made in’, ‘made from’, and ‘is in/at.’ 
4. Triples: Knowledge graphs are often illustrated as triplets: subject, predicate, and object. For instance, (Hazy, isaCA, atIBM). This format is the base of the Resource Description Framework (RDF) used in many knowledge graphs. 

Ontology 

Ontology is often at times confused with being a component or framework for a knowledge graph. There is still a debate as to how they differ from knowledge graphs. But boils down to, ontologies are a formal framework that talks about key ideas, categories, and relationships within a particular domain. 

It acts as a rulebook by telling which entity is what and what relation it has with the other entity. It is a schema that organizes and gives meaning to data. These are especially required for complex domains requiring formal semantics and reasoning. 

Use Cases of Knowledge Graph

Graph representation of data in AI is used across sectors and industries:

Search engines

Knowledge graphs are optimized by leading tech companies to make the search experience better. For example, a restaurant connects customer preferences, past bookings, seasonal data, and local delicacies to offer hyper-personalized suggestions. It goes beyond keywords and driver context-based recommendations. 

Data integration

Pharmaceutical companies have incorporated knowledge graphs to bring together research and development data across drug discovery, clinical trials, regulatory documents, and academic literature. This allows short research cycles with enhanced decision-making in real-time. 

Entertainment

This application of the knowledge graph also combines with its use case in recommendation systems. For example, on Netflix, it can understand the relationship between users, products, and content. And gives the most suitable recommendation. 

Data integration & analytics

It supports the connection of siloed data sources for joint analytics and business intelligence. 

NLP & AI

NLP means natural language processing, the knowledge graphs to understand and reason about information contextually for completing tasks such as Q&A and dialogue systems. 

Benefits & Challenges in Knowledge Graphs

Conclusion

Knowledge graphs for AI are those maps that artificial intelligence systems use to understand the relationships (edges) between real-world entities (nodes) and their different aspects in data. I have mentioned how these graphs work along with their core components. We also discussed the application and use cases of the same. Though knowledge graphs are beneficial, they come with some challenges in designing and implementing, as discussed. 

Let me know in the comments how soon you think AI will work exactly like humans? Keep reading, keep growing, and keep the curiosity alive!

Read Next: Data Science vs Machine Learning vs AI