Database models have gone through incredible growth amidst recent technological developments. Gone are the days when businesses and organizations relied solely on relational databases to store and manage their data. Today, as the amount and complexity of data are growing, we need a more flexible and dynamic way to represent and analyze relationships between data points. Enter knowledge graphs–a modern data management topology quickly gaining popularity among data-driven businesses.
But how do knowledge graphs differ from relational databases? And when should you use them? This article will delve into the key differences between knowledge graphs and relational databases and analyze their unique use cases. We will also discuss real-world applications where these databases can be seen in action, independently, and in conjunction.
What is a Knowledge Graph?
A knowledge graph is a visual representation of real-world data in a graphical format. It consists of nodes and edges that represent data entities and their relationships. The underlying knowledge base of a knowledge graph is called a graph database, a type of NoSQL database.
In a knowledge graph, a real-world entity, such as people, places, and events, is represented as a node. The relationships between data entities are represented as edges that connect the nodes. For example, a knowledge graph could represent the relationships between Drugs and Diseases:
where ‘Metformin’ and ‘Diabetes’ are the nodes, ‘treated by’ is the edge that connects the nodes, and ‘ID’, ‘Name’, ‘Description’, ‘Source’, are attributes of both entities.
A knowledge graph stores both entities and their relationships in the database.
What is a Relational Database?
Relational databases organize data into one or more tables, each representing a collection of related data. They store data in a structured manner, using a set of rules called a schema, which defines the tables' data types, relationships, and constraints. The data can be accessed from the database through a query language, SQL, to retrieve, update, and manage data consistently.
In a relational database, each table represents a specific entity, such as drugs or diseases. Each row in a table represents a unique instance of that entity, and every column represents a particular attribute or characteristic of an instance. For example, an drug table might include columns for drug id, generic name, brand names, and description. For example, a record of Metformin in the drug table could look like this:
|Drug ID||Generic Name||Description||Brand Names|
|DB00331||Metformin||Metformin is a biguanide antihyperglycemic used in conjunction with diet and exercise for glycemic control in type 2 diabetes mellitus. It is also used off-label for insulin resistance in polycystic ovary syndrome (PCOS).||Actoplus Met, Avandamet, Fortamet, Glucophage, Glucovance, Glumetza, Glycon, Invokamet, Janumet, Jentadueto, Kazano, Kombiglyze, Komboglyze, Qternmet, Riomet, Segluromet, Synjardy, Trijardy, Velmetia, Xigduo|
The relationships between data entities in a relational database are inferred from the tables instead of being separately stored in the database.
What Are the Similarities Between Knowledge Graphs & Relational Databases?
Before getting into the differences between knowledge graphs vs. relational databases, you need to understand the key similarities they share:
- Data Integration: Both knowledge graphs and relational databases can be used for data integration by taking data from different sources and combining them into a single database, allowing for easier access and data analysis.
- Data Management: Both are data storage and management tools that ensure your data's accuracy, consistency, and security by enforcing rules for how data can be stored and manipulated.
- Data Relationships: Both graph and relational databases establish the structure and relationships of the data and regularly maintain and update these definitions to ensure the freshness and accuracy of data.
- Query Language: Both knowledge graphs and relational databases have a query language that allows you to perform actions on data, such as retrieval from the database. In a knowledge graph, the query language might be SPARQL or another graph query language, while in a relational database, the query language is typically SQL.
- Horizontal Scalability: Both knowledge graphs and relational databases can be scaled horizontally across multiple machines to handle large datasets and high traffic levels.
What Are the Differences Between Knowledge Graphs & Relational Databases?
Now that we have covered the similarities let's dig into the differences between knowledge graphs and relational databases:
- Purpose: The purpose of a knowledge graph is to represent complex relationships and semantic meaning in a domain-specific context to derive meaningful insights from data. On the other hand, the purpose of a relational database is to manage large volumes of structured data in the form of rows and columns for analytical and operational use.
- Data representation: In a knowledge graph, data is represented as a graph with nodes representing entities and edges representing relationships between entities. In a relational database, data is described as connected tables with defined rows, columns, and relationships between tables.
- Schema: A knowledge graph is built on a flexible, schema-free graph database that allows you to dynamically change the information about an entity as new relationships are discovered. Conversely, a relational database is schema-driven, where a specific data structure and format are defined before inserting data. This rigid schema-based design makes relational databases challenging to update as new information becomes available.
- Performance with large datasets: Knowledge graphs are optimized for traversing large graphs with complex relationships. Their underlying graph databases can handle fast querying even for large datasets. In contrast, relational databases can only be optimized to handle large volumes of structured data by using efficient schemas. They cannot match the query performance of knowledge graphs for large unstructured datasets.
- Maintenance: The schema-free design of knowledge graphs makes them more flexible and easy to update dynamically as new relationships are discovered. Any new information can be added to a knowledge graph without disrupting existing data. On the other hand, relational databases require significant effort to maintain and update the schema as the data changes. Accommodating the slightest change to the data structure in a relational database requires the entire schema to be updated.
- Complexity: Knowledge graphs are designed to represent complex relationships between entities in a domain-specific context. Relational databases, however, are designed to manage simple relationships between data points.
What Are the Use Cases of Knowledge Graphs?
Here are a few of the many use cases of knowledge graphs, along with real-world examples:
- Recommendation Systems: Knowledge graphs can be used to power recommendation systems that provide personalized recommendations to users based on their preferences and streaming behavior. For example, Netflix uses a knowledge graph to recommend movies and TV shows to its users based on their past viewing history, ratings, and other data points.
- Semantic Search: Knowledge graphs can be used to power semantic search engines that can understand the meaning and context of user queries to provide more relevant search results. For example, Google's Knowledge Graph powers its semantic search capabilities by organizing information about entities, facts, and relationships into a graph structure.
- Fraud Detection: Knowledge graphs can be used to detect and prevent fraud by analyzing complex relationships and patterns in large datasets. For example, the European Union uses a knowledge graph to detect fraudulent activities in the financial sector by analyzing transactions, entities, and other data points.
- Healthcare: Knowledge graphs can be used to improve patient outcomes and drive research in the healthcare industry by analyzing complex relationships between medical conditions, symptoms, treatments, and other factors. For example, the Mayo Clinic uses a knowledge graph to identify potential drug interactions and treatment options for cancer patients based on their genetic profiles and medical history.
What Are the Use Cases of Relational Databases?
Relational databases have been around for decades and are used by various industries for information storage and management. Here are some real-world examples of systems that use relational databases:
- E-commerce applications: A common use case of relational databases can be found in e-commerce for storing information like product catalogs, customer data, and order histories. For example, Amazon uses a relational database to manage its vast product catalog, customer orders, and shipments.
- Financial Data Management: Relational databases are used to store financial transactions, such as invoices, payments, and account balances. Goldman Sachs, a leading global investment banking organization, uses a relational database to manage its financial data.
- Content Management Systems: Relational databases are used to store and manage digital content, such as articles, videos, and images. WordPress is a popular example of a CMS that uses a relational database to store blog content and user accounts.
- Inventory Management: Relational databases are used to track and manage stock levels, order histories, and shipment tracking. Walmart is an example of one of the largest retailers in the world that uses a relational database to manage its inventory levels and product movements throughout its supply chain.
When to Use Knowledge Graphs vs. Relational Databases?
Here are some general guidelines you can follow to decide when to use knowledge graphs vs. relational databases:
|Knowledge Graphs||Relational Databases|
|Types of data||When you have large volumes of unstructured or semi-structured data||When you have large amounts of structured data at hand|
|Types of relationships||When your data entities are connected by complex contextual relationships||When your data entities are connected by simple, well-defined relationships|
|Purpose||When you require context-based decision-making or advanced analytics for knowledge discovery||When you require a structured storage of data with high-performance retrieval and querying for analytics|
|Data modifications||When your data is dynamic and requires regular updates||When you have relatively static data with infrequent updates|
How Do Knowledge Graphs and Relational Databases work together?
While knowledge graphs and relational databases have many differences, they are not mutually exclusive in the real world. They offer unique capabilities that can be leveraged to get the best of both tools. By combining them, organizations can create a robust data management system that enables efficient data storage and accessibility and deep analysis of complex relationships between entities.
One example of using the two together can be applied to the field of drug discovery to manage and analyze vast amounts of medical data related to drug targets, molecular structures, and other pharmacological properties. For example, knowledge graphs can be used to link data such as chemical structures, protein interactions, and clinical trials, to create an accurate picture of how a drug might interact with the human body. Relational databases can then be used to store and manage this graph data in a structured way, allowing researchers to query the data and identify meaningful patterns quickly. By integrating knowledge graphs and relational databases, Biopharma companies can gain a more comprehensive understanding of drug targets and potential treatment options.
Wisecube’s Knowledge Graph Engine
We can go on about knowledge graphs vs. relational databases; however, you can only leverage their true potential by using the right tools. Fortunately for you, Wisecube has your knowledge graph needs covered. Wisecube's Knowledge Graph Engine is an open-source, AI-enabled knowledge graph that uses ground-breaking AI and NLP techniques to deliver best-of-breed analytics.
Designed for modern data-driven industries, specifically Biomedical, that deal with a plethora of unstructured, disjointed data that is difficult to access, hampering research opportunities. Wisecube comes to the rescue with links to various biomedical literature sources and databases.
Quickly uncovering essential insights and making ground-breaking discoveries from your data is no longer a complex task. So, what are you waiting for? Get in touch with us today to innovate your data management and discover hidden gems in your data.