Businesses generously profit from widening their knowledge and understanding of how company data interconnects and relates. Knowledge Graphs can help you explore new information quickly and effortlessly to make knowledge acquisition and mapping more accessible. But what is a knowledge graph? And how can you use them to your advantage to help grow your company and streamline operations?
To begin with, knowledge models are central to knowledge graphs. The model contains brief descriptions of concepts, entities, and relationships that are intertwined with one another.
These graphs are visual representations of data created through graph data structures. The descriptions have formal connotations, allowing the user and the computer to easily comprehend them.
Descriptions are interdependent as they form a network. Each entity in the grid represents some part of the description of a related entity. Based on the model, data is interlinked and detailed through semantic metadata.
Additionally, knowledge graphs are similar to knowledge databases, but there’s often ambiguity about what they are and how they can be used help businesses
Table of Contents
What Are Knowledge Graphs?
Knowledge graphs are used to represent real-world entities like objects, events, situations, or concepts within a network and identify relationships among them. This data is stored inside a graph database and constructed through a graph data structure.
A knowledge graph is also known as a semantic graph because it follows universally acknowledged notations that are understood by the layman.
Finally, knowledge graphs consists of three primary elements: nodes, edges, and labels. Any real-life entity such as an object or person represents a node. An edge outlines how the nodes are related to one another. And labels define what a particular node represents.
How Do Knowlewdge Graphs Work?
Knowing what knowledge graphs are and how they work will help you understand data correlations at a glance. They’re constructed through various datasets that often differ in structure. The data is structured using schemas, identities, and context.
- Schemas model the architecture of the graph
- Identities correctly label the elementary nodes
- Context ascertains the backdrop of the nodes
These elements can even understand when words have multiple meanings. That’s how search engine algorithms differentiate between homonyms. For example, it identifies the word “Puma” as either an animal or a brand, depending on context.
Such graphs use natural language processing (NLP) to build extensive outlooks of the key elements (i.e., nodes, edges, and labels). This process is called semantic enrichment. It allows them to recognize discrete objects and determine the relationships between those objects. The knowledge acquired is then assimilated and consolidated with relevant existing datasets.
Data integration through these graphs allows for generating new knowledge and plotting connections between data nodes that were previously unknown.
Applications
Many businesses across a variety of industries use these diagrams to build datasets to map important relationships between relevant entities. Some of the core industries that use them include:
- Retail: This sector uses knowledge graphs to design up-sell and cross-sell strategies, which recommend products based on the user’s likes, whims, fancies, and previous purchases. They also monitor and maintain data on users’ purchase behavior and trends followed by demographic groups.
- Entertainment: The entertainment industry uses these graphs to recommend new content for users to watch or read based on their online behavior and preferences. AI-based recommendation engines use them on platforms like Netflix and YouTube.
- Finance: Graphing is also widely used in the finance sector to create KYC and anti-money laundering processes. It prevents online scams and fraud through financial crime prevention and investigation. It allows financial institutions to maintain data about the flow of money from the bank to the client and identify unauthorized customers.
Conclusion
Knowledge graphs are extremely helpful tools for users as well as designers. They help various industries to maintain and connect records about multiple entities. Without knowledge graphs, a simple internet search would not be possible.
