As humans, we are always curious about the world around us, and there is a myriad of information out there. We are always searching on the internet to make our lives easier, which leaves us with varied results. We hoard a lot of information, and cannot find relevant information when we need it. But I wondered what if a machine could do it for us, store information, classify information, and form easy relationships interconnecting one piece of information to another like a butterfly effect. For me, the concept of a Knowledge Graph serves the same idea, and here is what I have dwelled upon it.

Knowledge Graphs understand real-world entities and illustrate their relationships with one another. It was introduced by Google way back in 2012 but the term ‘Knowledge Graph’ was coined by Austrian Linguist Edgar. W. Schneider in 1972.

It’s a common saying that a picture is worth a thousand words and a knowledge graph is a visual representation explaining the relation between variable quantities. The information from real-world entities such as objects, events, situations, and concepts are stored in a graph database and visualized as a graph structure. It consists of the following:

• Nodes represent different and varied entities
• Edges show the relationship between 2 or more nodes
• Labels define nodes

Knowledge graphs are an effective tool for organizing, managing, and interpreting huge amounts of data. They record the interactions between distinct things and provide a more comprehensive and contextual understanding of data. It can be used to power a variety of applications. Knowledge graphs are changing the way we use and interact with data, from recommendation systems to healthcare. The value of knowledge graphs will only grow as the amount of data generated increases.

Unlike relational databases which store data in rows and columns, graph databases store data in nodes and form edges to explain the relationship between different nodes. This way it’s easier to understand and more scalable.

An early blog by Amit Singhal, former Senior Vice President of Engineering at Google, 2012 shed light on the need for Knowledge Graphs to improve data querying and how one entity (a piece of information) relates to another entity, and how all of that is connected to a different entity. It helps in identifying the relations and patterns to improve search queries and go deeper to further improve our search results

We are looking at a technology that can transform the way we analyze, predict, and come to decisions. Knowledge Graphs have the potential to revolutionize the analytics industry and radically transform decision-making by improving precision and scalability.

It is interoperable in nature and is widely used in many industries and there’s no question it’s the next big thing, though it has been around for quite a long time.