Knowledge Graphs

Knowledge Graphs

Knowledge graphs represent a powerful approach to organizing information in a way that captures not just data, but the relationships between different data points. Unlike traditional databases, knowledge graphs structure information as a network, with entities as nodes and relationships as connecting edges.

These sophisticated data structures enable machines to understand and process information in ways that more closely mimic human cognition. By connecting related concepts, knowledge graphs create context that allows for more intelligent data processing, inference, and discovery.

The Google knowledge panel of La Sagrada Familia includes an image of the site, a map, a description, address, hours of operation, the architects who built it, its height, and more.

You may have heard of knowledge graphs in the context of search engines. The Google Knowledge Graph changed how we search for and find information on the Web. It amasses facts about people, places, and things into an organized network of entities. When you do a Google search for information, it uses the connections between entities to surface the most relevant results in context, for example, in the box Google calls the “knowledge panel.”

Network of Entities

Network of Entities

The entities in the Google knowledge graph represent the world as we know it, marking a shift from “strings to things.” Behind this simple phrase is the profound concept of treating information on the web as entities rather than a bunch of text.

Since information is organized as a network of entities, Google can tap into the collective intelligence of the knowledge graph to return results tailored to the meaning of your query rather than a simple keyword match.

Triple

RDF Triples

RDF, which stands for Resource Description Framework, is a standardized method for expressing data in the form of a directed graph using subject-predicate-object statements, commonly referred to as “triples.”

The foundational unit of a knowledge graph is the triple. It comprises two nodes that represent entities connected by a single edge to articulate their relationship. Represented as “subject-predicate-object” statements, a triple illustrates how an entity (subject) links to another entity or a simple value (object) through a specific property (predicate).

Nodes (Entities)

Nodes

Nodes denote and store details about entities, such as people, places, objects, or institutions. Each node has a (or sometimes several) label to identify the node type and may optionally have one or more properties (attributes).

Nodes are also sometimes called vertices.

For example, the nodes in an e-commerce knowledge graph typically represent entities such as Person (customers and prospects), Products, and Orders:

Typical Retail Entities

Edges (Relationships)

Edges

Edges (relationships) link two nodes together: they show how the entities are related. Like nodes, each Edge has a label identifying the type and may optionally have one or more properties.

In the e-commerce example, relationships exist between the Person and Order nodes, capturing the “placed order” relationship between Person - customers - and their Orders:

As these triples combine, they form interconnected graphs of resources, laying the groundwork for a comprehensive knowledge graph. However, to provide meaning to the machine, you must express these triples in a machine-readable format.

You can express RDF triples in a variety of formats, including:

• Turtle

• RDF/XML

• And JSON-LD

JSON-LD defined Triple

Ontologies

Ontologies

An ontology is a formal specification of the concepts and the relationships between them for a given subject area; semantic networks are a common way to represent ontologies. Put simply, ontologies are a type of organizing principle.

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A model typically encompasses three key elements:

• classes, also known as types, representing categories of entities such as an organization, event, or person.
• attributes, aka properties, are used to describe an entity. For instance, a Person entity might possess a name as one of its attributes.

• relationships, which are also represented by properties, delineate how one entity connects to another. These are similar to attributes in that they describe an entity, but more specifically, they describe how one entity connects to another entity.

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Use Cases

Use Cases ..

• Fraud Detection and Analytics in Financial Services, Banking, and Insurance

  In Fraud Detection and Analyticsopens in new tab, the knowledge graph represents a network of transactions, their participants, and relevant information about them. Companies can use this knowledge graph to quickly identify suspicious activity, investigate suspected fraud, and evolve their knowledge graph to keep up with changing fraud patterns. Algorithms such as pathfinding and community detection provide key signals to machine learning algorithms that can uncover more sophisticated fraud networks.
• Master Data Management

  In Master Data Management (e.g., for Customer 360 use cases), the knowledge graph provides an organized, resolved (i.e., “de-duped”), and comprehensive database of a company’s customers and the company’s interactions with them.

  This organized view of customers is especially important for companies with multiple divisions or applications interacting with customers. Without a knowledge graph, it can be difficult or impossible to obtain an accurate view of the customer. A knowledge graph links customer behaviors across multiple applications through an organizing principle that identifies them as coming from the same customer.

• Supply Chain Management

  In Supply Chain Management, a knowledge graph represents the network of suppliers, raw materials, products, and logistics that work together to supply a company’s operations and customers. This end-to-end supply chain visibility allows managers to identify weak points and predict where disruptions may occur. Graph algorithms such as opens in new tabshortest path optimize the supply chain in real time by finding the most direct route between A and B.
• Drug Discovery in Healthcare Research

  Knowledge graphs store information about the research subject in medical and other research use cases. For example, the knowledge graph could have protein and genome sequences together with environmental and chemical data, revealing intricate patterns and expanding our knowledge of proteins.