What is Knowledge Graphs?

A knowledge graph is a way to organize and connect information so it’s easier to understand. It links related things like people, places, and events, helping us find useful insights. Big companies like Google use knowledge graphs to give direct answers in search results instead of just showing links.In this article, we will talk all about knowledge graphs, how they work, their use cases, and characteristics.

This article was published as a part of the Data Science Blogathon.

Understanding About Knowledge Graphs

A knowledge graph is a structured way to organize information using nodes (entities) and edges (relationships). It helps store and analyze connected data efficiently, making it easier for humans and software to understand. Unlike regular graphs, it encodes intelligence directly into the data. Knowledge graphs often use SPO triplets (e.g., Paris-CapitalOf-France) to represent relationships, following RDF standards.Here are some points you can go through to understand

1. Uses nodes (entities) and edges (relationships) to organize information.
2. Helps store and analyze connected data efficiently.
3. Encodes intelligence directly into the data for easier understanding.
4. Represents relationships using SPO triplets (e.g., Paris-CapitalOf-France).
5. Follows RDF standards for structured knowledge representation.

A sample knowledge graph of the following is shown in the figure below. Here the nodes represent entities, the edge labels represent types of relations, and the edges themselves represent existing relationships.

While the SPO triplets that can be extracted from the given knowledge are shown below:

Now we understand the structure of KGs. Next, we would look into the organizing principles of KGs, which bring out their essence and differentiate it from typical graphs.

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Organizing Principles of a Knowledge Graph

There are several ways to organize data in graphs, each with advantages and drawbacks. In this section, we will be discussing each of the organizing hierarchies. We would start with plain simple graphs and try to explain how adding successive layers of organization helps make the data smart and more interpretable, thereby helping solve increasingly sophisticated problems.

Plain Old Graphs

These are graphs that haven’t had any organizing principle applied to them. Still, we know that they help solve our daily challenges as they underpin some very important systems. Instead of associating the “organizing principles”‘ with the data, the programs and systems that consume these graph data are embedded with the “organizing principles.”

A typical example of the same would be the sales of an online store. The figure below shows a small portion of the sales and product catalog graph, showing the customers and their purchases in the form of a plain old graph.

• Looking at the graph directly may not be easy to understand.
• P nodes represent products, C nodes represent customers, and connections between them represent purchases.
• With this knowledge, the program can easily answer questions like:
  • Which products a specific customer bought.
  • Which customers bought a specific product.
  • The popularity of a product.
• Graph data is compact and useful, but it can be challenging for data scientists unfamiliar with the domain.
• Without prior knowledge, they may need explanations or have to reverse-engineer the code to interpret the data.
• A better solution is to organize the graph data using specific principles, which will be discussed in the next sections.

Richer Graph Models

The first organizing principle that we would see is the property graph model. It is richer and far more organized and supports labeled nodes, types, and directions of relationships and properties (key-value pairs) on both nodes. Thus it can provide humans and machines with some essential clues about the information it contains. Thus this organizing style makes the graph self-descriptive to a certain level and is a clear step towards making the data smarter!  Also, some preprocessing and visualizations can be carried out without any domain knowledge just by leveraging the features of property graph models.

The figure above shows an enriched view of sales and product catalogs, which include labels, properties, and named relationships.

Use Cases of Knowledge Graphs

Here are some key use cases of knowledge graphs:

1. Links Information: It connects facts, like showing how “Tom Hanks” is related to the movie “Forrest Gump.”
2. Better Search: It helps Google or other tools give direct answers, like “How tall is Mount Everest?”
3. Smart Suggestions: It powers recommendations, like suggesting songs on Spotify or products on Amazon.
4. Helps Computers Understand: It organizes data so machines can see relationships, like linking diseases to their symptoms.
5. Used in Many Fields: It’s used in healthcare, shopping, social media, and more to make things smarter and easier.

How Does a Knowledge Graph Work?

A Knowledge Graph is a structured representation of knowledge that integrates information from various sources to create a network of interconnected entities and their relationships. Here’s how it works:

1. Collect Data: Gather information from different sources like websites, databases, or documents.
2. Identify Things and Connections: Find key items (like people, places, or events) and how they relate to each other.
3. Build a Network: Create a map where items are points (nodes) and their relationships are lines (edges).
4. Store and Organize: Save this map in a way that makes it easy to search and update.
5. Use for Answers: Ask questions or get insights by exploring the connections in the map.

Example:

Consider a Knowledge Graph about movies:

• Entities: “Tom Hanks,” “Forrest Gump,” “Robert Zemeckis.”
• Relationships: “Tom Hanks → starred in → Forrest Gump,” “Robert Zemeckis → directed → Forrest Gump.”
• Attributes: “Forrest Gump → release date → 1994.”

By connecting these entities and relationships, the Knowledge Graph enables powerful queries like “Which movies did Tom Hanks star in?” or “Who directed Forrest Gump?”

Knowledge Graphs Using Ontologies for Multilevel Relationships

Taxonomies help organize by bringing in the subcategory_of relations; Ontology allows define more complex relationships between categories like part_of, compatible_with, and depends_on. Thus following the ontological instructions, we can not only explore the categories vertically (hierarchically), but it also allows for horizontal comparison. Besides this, they can be built in a modular fashion to make them more compact with sophisticated use of layering. Thus ontology helps make knowledge actionable. The figure below is an ontological representation showing the upgrade paths for products in a category.

Thus till now, we have seen different types of organizing principles of KG. However, the organizing principle we choose to use should always be driven by its intended usage. It is advisable not to build rich and overcomplicated features into the organizing principles if no associate processes or agents would use them. It is a common mistake to opt for an overly ambitious organizing principle as it would be costly in terms of resources and time.

How to Implement Knowledge Graphs?

It is noteworthy to mention that these steps are not discrete and may vary depending on the specific use case and technology. Additionally, libraries and frameworks like OpenAI, GPT 3, and Google’s Tensor can help with the steps.

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Where would you Find Knowledge Graphs in the Real World?

Now we know how to build KG, it would be interesting for you to be aware of the usage of KG. 

• Fraud Detection: Knowledge graphs visually represent fraud scenarios, helping financial consultants enhance machine learning algorithms by incorporating diverse datasets. For example, if two customers share the same email address, it could indicate fraud, even if traditional models overlook this detail.

• Data Governance: Knowledge graphs act as a semantic layer, organizing metadata and relationships to improve data quality and consistency. They help identify duplicates or inconsistencies by visualizing interconnected data, enabling better analytics and usability.

• Managing Information: In finance, knowledge graphs like Thomson Reuters’ provide a comprehensive view of the financial ecosystem, streamlining investments and research. They integrate data on organizations, people, transactions, and more, serving as a foundation for risk assessment and decision-making.

• Insider Trading: Knowledge graphs simplify the detection of insider trading by connecting various data sources (e.g., calls, emails, messages) and revealing hidden patterns. This approach is more efficient than traditional methods, making it easier to identify information leaks and relationships.

Conclusion

Knowledge graphs organize and connect data using structured relationships, enabling smarter insights. They follow specific principles and often use ontologies for multilevel connections. Their applications range from search engines to recommendation systems. Implementing them requires proper structuring and integration. Real-world examples include Google Search, healthcare, finance, and AI-driven solutions.

Thus today, we have looked deeply into making our data more intelligent and smart. The technique that we utilized for the same is Knowledge Graphs. To briefly summarized today’s read, the key takeaways for you in this article would be:

• How Knowledge Graphs differ from normal graphs because of the addition of “organizing techniques.”
• We then looked into each of the organizing techniques in depth, explaining each case with our analogy of online sales of a shop.
• We followed it by building Knowledge Graphs and where we can find them in the real world.
•  Finally, we ended with some additional information on Scene Graphs which are leveraged when we come across image and video data.

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Neil D

Advancing language model research by day and writing about my work online by night. I explore AI breakthroughs and transform complex studies into clear, engaging insights that empower professionals and enthusiasts alike.

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