There is a lot of data scattered throughout an organization. Much of the time, you'll hear that businesses are using this data to be a "data-driven" organization. Although I'm sure they are using some of the data, much is siloed and without full context. In many data science initiatives, bridging these silos is crucial for deriving accurate insights. Much of this is due to how traditional relational databases often struggle to capture the complex relationships and interconnectedness inherent in real-world data. This limitation has spurred the rise of knowledge graph databases, the answer to many of the problems of the more traditional approach. Knowledge graphs are not a new idea, most notably, the Google Knowledge Graph demonstrated the power of connecting entities in a more semantic way, revolutionizing how search results are delivered.

With a knowledge graph database, organizations can go beyond storing simple data points and move to a more holistic approach to representing information that better captures the network of interconnected entities and the relationships within the data. This approach unlocks deeper insights, provides access to more effective analysis, and enables the development of intelligent applications that can understand and reason about data more humanistically.

In this guide, we explore the core concepts, benefits, and challenges of knowledge graph databases and examine their impact on data management. We also explain how to build a knowledge graph database using your existing data and infrastructure. Let us begin with the fundamentals of this approach.

What is a Knowledge Graph Database?

A knowledge graph database organizes data as a network of nodes and edges. Nodes represent entities like people, places, or ideas, while edges show the connections between them. A knowledge graph database is typically built on an underlying graph database. Although you might hear about knowledge graphs and property graphs on SQL or NoSQL platforms, you get the most out of this approach when the data is stored in a dedicated graph database or accessed using a specialized graph query engine. In a property graph model, each node and edge can include extra details that provide context about entities and their relationships. This structure mirrors real-world connections and aligns with the semantic web's goal of creating meaningful links between data for better understanding.

Conceptually, you can think of a knowledge graph as a network of data versus columns, rows, tables, etc. The two main features of this network are:

• Nodes: These represent entities (people, places, things, concepts).
• Edges: These represent the relationships between the entities (e.g., "works at," "located in," "is a type of").

This structure allows the knowledge graph to capture the rich context and meaning behind data, moving beyond simple tables and rows to represent information in a way that mirrors how humans understand the world.

Unlike traditional databases that focus on storing individual data points in isolation, knowledge graph databases emphasize the connections between data points. This interconnectedness means that querying, analysis, and reasoning go beyond the limitations seen with other database technologies. The result is the ability to uncover hidden patterns, infer new knowledge, and gain a more holistic understanding of the data in a whole new way. Of course, all of this sounds great, but why are knowledge graphs any more important than traditional ways to store and query data?

Importance of Knowledge Graphs in Data Management

Knowledge graphs are becoming increasingly important in data management due to their ability to address the limitations of traditional approaches. As datasets grow larger and more complex, so do the relationships between disparate points of data. This is amplified by data being scattered across various sources and formats. A more flexible and interconnected approach to data management becomes crucial. Knowledge graphs offer several key advantages for these particular use cases:

• Contextualization: Knowledge graphs provide context to data by explicitly representing relationships between entities. Instead of relying on complex queries with lots of joins between different tables, a graph enables the user to see the entire picture. This context is essential for understanding the meaning and significance of data points, opening new avenues for analysis and decision-making.
• Flexibility: Knowledge graphs are highly flexible and adaptable, accommodating diverse data sources and formats. They can easily incorporate new information and evolve as the data landscape changes. The graph itself can be extremely dynamic and use the latest data being added to the graph to further uncover or enhance understanding.
• Semantic Understanding: Knowledge graphs open up a new front for enabling machines to understand the meaning of data. Since the data within the graph is represented in a way that is closer to human cognition, machines can process and use the data in a more human-like way. This semantic understanding is essential for developing intelligent applications that can reason about data and perform complex tasks.

Knowledge graphs offer a more intuitive, adaptable, and intelligent approach to data management. This means that data can have a much greater impact than when analyzed in a more traditional way, such as with SQL queries and analytics. For organizations aiming to unify their data strategy, building an enterprise knowledge graph can effectively integrate data from multiple departments, eliminating silos. Now that we've looked at why knowledge graphs and their underlying graph database are important, let's move further into looking at the exact benefits they bring with a more technology-centric lens.

Benefits of Knowledge Graph Databases

If you've tried to extract the insights you'd expect from a Knowledge graph out of a traditional database, you quickly realize how much more efficient a graph database is for facilitating such queries.  This is especially evident in an enterprise knowledge graph setup, where vast amounts of data must be queried seamlessly. With knowledge graphs and graph databases, you get a wide range of benefits across various domains and applications. Building on some of the previous points mentioned above, here are a few benefits of adopting a knowledge graph:

A. Unified Data View

The graph database and model that hosts a knowledge graph excel at integrating data from diverse sources. This includes your traditional sources like relational and NoSQL databases, as well as data from APIs and unstructured data like text documents. By representing these various types of data as a unified graph, they break down data silos and enable a holistic view of the data and all the relationships within it. This allows for more comprehensive analytics capabilities, which help create more informed decisions based on the data.

B. Enhanced Semantic Search

Knowledge graphs enable more intelligent and context-aware search capabilities. Compared to SQL and NoSQL queries that solely query based on simple criteria such as matching keywords, semantic search algorithms can leverage the relationships and context within the graph to understand the intent behind a query and deliver more relevant data. The result is a more intuitive and effective search experience.

C. Relationship Discovery

The interconnected nature of knowledge graphs makes them ideal for uncovering hidden relationships and patterns within data. The great part about graphs is that they can be queried through languages such as Cypher or Gremlin but can also be explored visually with a graph visualization tool. Regardless of how it's done, by traversing the graph and analyzing connections, organizations can identify previously unknown associations and gain new insights, potentially even making more accurate and informed predictions. This capability is invaluable for data science teams investigating hidden correlations that could lead to breakthroughs in analytics or predictions.

Here is a Cypher code example that demonstrates relationship discovery by finding potential new connections between people based on mutual friends:

MATCH (p:Person {name: 'Alice'})
  -[:FRIENDS_WITH]->
  (friend:Person)
  -[:FRIENDS_WITH]->
  (potential:Person)
WHERE NOT (p)-[:FRIENDS_WITH]->(potential)
RETURN potential.name AS SuggestedConnection,
       friend.name AS MutualFriend

D. Facilitation of Machine Learning and AI

Lastly, knowledge graphs also provide the structured and semantically rich foundation that can help machine learning and AI applications thrive. They are also becoming a crucial asset in data science workflows, aiding in the exploration and interpretation of complex datasets. The explicit representation of relationships and context within the graph enables algorithms to learn more effectively and, because of the predictive nature of LLMs and related AI platforms, make more accurate predictions. By including a knowledge graph as part of the architecture, organizations can enhance the performance of various AI tasks, such as natural language processing, recommendation systems, and fraud detection.

Although there are benefits beyond the four discussed here, this covers the main bases that are widely applicable. Although production-ready AI is just starting to surface and become more common, many of the most powerful AI tools out there are already leveraging knowledge graphs to push these solutions to the next level. Besides these benefits, though, there are also some challenges when it comes to creating and scaling out full-on, production-grade knowledge graphs.

Challenges in Implementing Knowledge Graph Databases

While knowledge graph databases offer significant advantages, their implementation can present certain challenges. Although some are very specific to knowledge graphs and graph databases, I would argue that whether moving data to a SQL-based Big Data platform or a graph-based one, some challenges are universal. Here are four challenges to be aware of and some tips on mitigation or ways to reduce the impact.

A. Data Modeling

Although moving different types of data between data models is tough, moving data into a graph model requires a significant paradigm shift. Beyond just mapping field-to-field, designing an effective knowledge graph schema requires careful consideration of the domain and the relationships between entities. This can be a complex process, particularly for large and intricate datasets. There are tools out there that can assist with this, though. For instance, PuppyGraph has an automated schema creation tool that will analyze the underlying data store and generate a model that will capture the data and context of that data. Although sometimes you may need to add in a human touch to get the final details correct, these types of tools will give you a good head start on the journey.

B. Data Migration

Once you've sorted out your data model, you also need to find a way to get the data into the graph. Migrating existing data from traditional databases to a graph-compatible format can be a time-consuming and resource-intensive task. With traditional graph databases, this means that you'll likely need to set up extensive ETL (extract, transform, and load) pipelines to get the data into the database. From these pipelines, data is replicated into the graph database, meaning that you now have two copies of the data. Unlike SQL-based ETL tools, where there is an abundance, there are fewer graph ETL tools available, and many are highly specialized. Luckily, some solutions make this easier than others. PuppyGraph uses a different approach which involves directly connecting to the data source so that no ETL is actually needed. We will show you more about the exact mechanisms a bit further down in the blog.

C. Scalability

A well-known problem with traditional graph databases and knowledge graphs is that they don't tend to scale well. As knowledge graphs grow in size and complexity, ensuring scalability and performance can be challenging and sometimes downright impossible. Increasing data volumes can quickly move queries from sub-second latency to taking minutes or longer. This is extremely true with more complex, multi-hop queries. To keep performance at a high level, traditional graph databases require extensive efforts to scale, potentially increasing budgets for increased hardware and licenses.

D. Schema Evolution and Maintenance

Knowledge graphs are flexible, but that flexibility can lead to difficulties when your data or business requirements change. Updating the graph schema without disrupting ongoing operations is a complex process. Adjustments may be needed as new data sources are added or as relationships evolve, requiring careful planning and rigorous testing. Organizations must implement clear strategies and version controls to manage schema changes effectively while ensuring backward compatibility and minimal downtime.

Choosing the right knowledge graph database technology requires evaluating various factors. There are many graph database options to create knowledge graphs upon, all of which require factoring in variables such as performance, scalability, features, and vendor support. Not to mention a few others, such as data model and migration support, which we covered in previous points.

So is there an easier way to harness the benefits of a knowledge graph without the complexity? As I already alluded to, of course, there is! That's where PuppyGraph's zero-ETL graph query engine comes into play.

Build Knowledge Graphs Without a Graph Database

While dedicated graph databases offer optimized performance and features for managing knowledge graphs, it's also possible to build knowledge graphs using alternative approaches. These methods may involve leveraging existing relational or NoSQL databases with graph-like capabilities or utilizing specialized graph libraries and frameworks.

However, these approaches often come with scalability, performance, and graph-specific functionality limitations. They may require significant customization and engineering effort to achieve the desired results and even then, most don't perform quite as well as using a native graph database.

So, quite obviously there is a gap between knowledge graphs built upon true graph databases and platforms that have graph-like capabilities. But what if there were a better way to get the benefits of a true graph database solution without the overhead? This gap is where we explore PuppyGraph, which is a more streamlined alternative to all the traditional ways that knowledge graphs are normally built. Designed with ease of use and developer experience in mind, PuppyGraph's graph query engine offers a faster and more efficient way to knowledge graph development without compromising on performance or scalability.

Here's what sets PuppyGraph apart:

• No separate graph databases required & zero ETL: With PuppyGraph, you can directly connect your SQL data stores to our graph query engine and instantly query your data as a graph, no graph database or ETL required. This means that the knowledge graph acquires data directly from the underlying data store without complex ETL, one of the biggest hurdles in graph database adoption.
• Easy deployment and set up: To begin querying your data as a graph, simply deploy PuppyGraph (via Docker or AWS AMI), connect to your data sources and map your data into the graph, and begin querying in just a few minutes.
• Flexible data model: PuppyGraph supports a flexible data model and graph schema. This means that as your data changes, your graph schema in PuppyGraph can easily accommodate these changes with minimal effort.
• Horizontally scalable for maximum speed: Need to process queries petabyte level dta quickly as data expands? PuppyGraph’s compute engine is distributed, allowing you to easily add more nodes to your cluster as your data grows or processing requirements change (more machines -> better performance). The distributed design allows PuppyGraph to handle huge size of data and complex queries like 10-hop neighbor queries in 2.26 seconds. 

With PuppyGraph, developers can focus on building their knowledge graph applications without getting bogged down in the complexities of database management. With an easy-to-use UI, flexible data model, and performance that can outshine even the most reputable graph databases, PuppyGraph is the go-to solution for a wide range of use cases, from small-scale projects and research to large enterprise deployments. By simplifying knowledge graph development and providing a scalable and performant platform for managing graph data, PuppyGraph gives organizations a clear path to unlocking the full potential of their data through graph technologies.

Conclusion

Knowledge graph databases represent a significant advancement in data management, offering a more intuitive, interconnected, and intelligent approach to handling information. By representing data as a network of entities and relationships, organizations can gain a deeper understanding of their data, uncover hidden patterns, and develop more intelligent applications.

As the volume and complexity of data continue to grow, knowledge graph databases will play an increasingly critical role in enabling organizations to extract meaningful insights, drive innovation, and gain a competitive edge. By embracing this technology, businesses can navigate the complexities of the data-driven world and unlock new possibilities for growth.

While implementing and using a knowledge graph databases can present challenges, solutions like PuppyGraph are emerging to simplify the process and make graph technology more accessible. Need to find the right knowledge graph database technology for your project? Try out PuppyGraph's graph query engine and create knowledge graphs in minutes. Start out with our free-forever developer offering or book free demo with our graph expert team.