As generative artificial intelligence (genAI) and advanced data analytics have exploded within the past few years, more and more people are talking about knowledge graphs. But what are knowledge graphs? And more importantly, why do they matter? 

Let’s start with a basic definition. According to IBM, a knowledge graph (also known as a semantic network) “represents a network of real-world entities – such as objects, events, situations or concepts – and illustrates the relationship between them.”

Why are they important? In a nutshell, knowledge graphs are relevant to anyone looking to squeeze more insight and value from their data. Organizations can use knowledge graphs to capture their data’s real-world meaning and ensure everyone can access and exploit it. Knowledge graphs eliminate data silos and provide data-driven AI applications with the breadth, depth, and nuance necessary to generate more accurate, more relevant, and more sophisticated outputs. Best of all, knowledge graphs are no longer the preserve of specialist data scientists. The latest data analytics tools and platforms make knowledge graphs easy for users of all skill levels – including novices and non-coders. 

Let’s take a deeper dive into knowledge graphs: what they are, how they’re useful, and how they’re poised to change data analytics and AI for users around the world.

Within Data, Context is Everything

The most valuable bits of data are rarely unambiguous – context is everything. Knowledge graphs are significant because they bring crucial context to datasets and data operations. Take the famous 2019 case of a 911 call made by a woman reporting a domestic abuse incident, for example. 

The caller tells the emergency dispatcher she wants to order a pizza. The dispatcher explains that 911 is for emergency calls, not pizza delivery. But when the caller continues asking for pizza despite the dispatcher’s appeals, the dispatcher quickly recognizes the caller’s real meaning – her mother is at immediate risk from her abuser. Using nothing more than the everyday language of a pizza order, the caller is able to explain that the perpetrator is still in the house and that she urgently needs police help. Here’s an excerpt from the full call:

Dispatcher: Oregon 911.

Caller: I would like to order a pizza at [address redacted].

Dispatcher: You called 911 to order a pizza?

Caller: Uh, yeah. Apartment [address redacted].

Dispatcher: This is the wrong number to call for a pizza...

Caller: No, no, no. You're not understanding.

Dispatcher: I'm getting you now.

The call demonstrates the complexity of human communication, and how time, place, and relationship can change the meaning of a simple string of words. However, machines and algorithms struggle at deciphering and detecting this nuance and complexity. That’s why the search for techniques and technologies that enable machines to better comprehend the semantics of data is one of the most important areas of focus in data science and AI. This is precisely where knowledge graphs come in. 

How Knowledge Graphs Connect the Dots

As the name suggests, a knowledge graph captures and represents data in the form a graph. This graph comprises nodes (the entities of interest and their uses), their attributes, and the relationships between them. Crucially, the structure of the knowledge graph enables associations and links to be made between different data components; the graph looks like a network of endlessly connected points. This multi-layered approach provides the depth and flexibility that transforms data into knowledge while capturing the nuances of context and semantics. 

With a knowledge graph, bringing together data from diverse sources and in different forms is straightforward and amplifies data’s value. This approach contrasts with many data management tools, where databases sacrifice semantics for the sake of conformity across disparate data formats. Knowledge graphs also embrace the dynamic and changing nature of knowledge, which always evolves as new information arises.

Ontology is another important concept here. Broadly speaking, a knowledge graph acquires and integrates data into an ontology, which defines domain knowledge through definitions, rules, relationships, and uses. An ontology therefore provides a shared vocabulary that can align with other applications and make the graph’s knowledge available for further analysis and processing.

All this brings to the fact that a knowledge graph is not an end in itself. For enterprises, a knowledge graph is merely a means for creating a data fabric: a rich, flexible, machine-readable layer that extends across an organization’s data infrastructure. This data fabric is what can be better exploited by other data science and AI platforms. 

Sharpening GenAI Outputs

For enterprises looking to leverage knowledge graphs’ ability to bring data together and apply multi-layered understanding, there are plenty of avenues of interest. For instance, knowledge graphs offer a number of ways to sharpening the accuracy and relevance of genAI outputs. Notably, knowledge graphs help large language models (LLMs) take advantage of new and up-to-date information, and work well with the vector embeddings utilized by retrieval-augmented generation (RAG), another key area of genAI development. Use cases also include Graph RAG: the fine-tuning of existing LLMs or training new models using specific domain expertise.

Outlining the key features and benefits of a knowledge graph is relatively straightforward. But building a graph from scratch is a completely different matter. Fortunately, no-code tools like Altair® Graph Studio™ can bridge the gap. Novice and expert users alike can construct knowledge graphs from their organization’s entire pool of enterprise data. Graph Studio applies a semantic, graph-based data fabric layer to this infrastructure. This eliminates silos and empowers both users and data/AI tools alike to understand business data’s real meaning.

Knowledge Graphs: Making an Impact Now

The 911 call mentioned earlier is just one illustration of the challenges involved in helping AI behave more dynamically, like human intelligence. Knowledge graphs are a key part of that journey, already playing an increasingly important role in bringing greater context to data. And of course the emergence of tools like Graph Studio are highlighting another equally powerful trend: the drive to democratize data science and AI tools and their capabilities. 

Graph Studio is part of the market-leading Altair® RapidMiner® data analytics and AI platform. For nearly four decades, Altair has helped organizations around the world solve their most pressing challenges. Click here to discover how Altair’s data analytics and AI capabilities can help any organization achieve its most difficult, most exciting goals.