What is a Knowledge Graph?
A company probably has lots of data from multiple sources. This could be internal employee data, customer data, data from public sources or even data the company pays to acquire.
To get value from this data, it normally needs to be channeled through a pipeline that transforms it and eventually presents it in a manner that is usable. Now, for a long time, data management systems have always been in formats that are very complex. You are either plowing through thousands of rows of data or searching through multiple tables to combine different pieces of data to get a result.
But what if it was possible to visualize all the data instead of packing it in multiple tables that make management a complex affair? This is where knowledge graphs come in.
Keep reading as we explore knowledge graphs.
What is a Knowledge Graph?
In simple terms, a knowledge graph is a knowledge base with a graph structure. It utilizes a graph database and graph interface for data storage and visualization, respectively.
While the term has been around since as far back as 1972, the knowledge graph was popularized by Google in 2012 in a bid to replace its traditional search system. It’s no wonder then that when you google the phrase “knowledge graph”, one of the top results you will see will probably be the popular Google's knowledge graph.
We choose to use Google’s Knowledge graph to demonstrate the mighty value of knowledge graphs for a good reason. You see, Google has utilized knowledge graphs to great effect and this could as well be one of their powerful secret cards.
In fact, Google admits in their own blog that “The Knowledge Graph enables you to search for things, people or places that Google knows about—landmarks, celebrities, cities, sports teams…”.
This is a great revelation of what a knowledge graph can accomplish when built right and put into serious use. In essence, what Google means by “things, people or places” that they know is hsimply data. So they basically take this data and put it in a knowledge graph, and this helps them to drive many of their successful products like the search engine.
Rather than accessing and presenting queried data in the traditional rigid manner, knowledge graphs add meaning to queries and data. For instance, traditional search algorithms (like those used in many eCommerce stores) only present data that fits queries exactly. For example, a search query for “bag” brings all results with the “bag” keyword in them.
Semantic algorithms, on the other hand, understand the query, give context to the query, and present dynamic results that fit the query best. A query for “bag” acquires results in the context of “what is a bag” or “different types of bags”. This is made possible thanks to Knowledge Graphs.
Also Read: Knowledge Graphs and Large Language Models
Why does your organization need a knowledge graph?
In the same way that Google is benefiting from the knowledge graph, you too can take all the data that your company possesses, put it into a knowledge graph and use it to drive great products for your customers or even for internal use.
In addition to improving search,
- Knowledge graphs can help in the storage of microdata in machine and human-readable forms
- They simplify large data sets
- They enrich the knowledge base. You don’t just define relationships but also expose hidden relationships between data. With this, complex customer needs are revealed for more effective product recommendations, and business research generally becomes easier and more comprehensive
- Knowledge graphs also help you track the flow of an element, which is useful for monitoring solutions. A financial organization, for example, will more easily track the flow of money. A cybersecurity solution can more easily identify affected systems by tracking the flow of a threat.
One key thing that a knowledge graph does is that it makes it very easy to visualize data. This is possible because of how graph databases work - through connections. If, for example, you run an eCommerce website that sells different types of phones and there is this customer known as ‘John’ who loves to buy a Samsung phone every time the company releases a new phone, you might want to discover which other products John loves to buy so that you can make recommendations. To do this, you will need to connect John to all the other products he has been buying. Once you find the other products that John is fond of buying, you might want to see if there are other customers that have the same traits as John. This requires a connection of all the customers that buy a Samsung phone every time a new one is released. Now, a knowledge graph makes it easy to visualize all these connections, unlike traditional database approaches. If you were to use a traditional database, you will probably need to join multiple tables and do some aggregation. It’s too much work. You will have customers in one table, products in another table, their location in another table, etc. But a graph database will have all this information in one place, enabling you to build connections and derive insights.
Also Read: Using Knowledge Graph to Detect Fraud