Read about the significance of data fabrics and knowledge graphs in modern data management to address the issue of complex, diverse and large-scale data ecosystems
Imagine starting your day with a friend who’s always looking out for you. For many of us that is already happening the moment we enter our cars. Seemingly overnight, our cars have become quite smart, with a plethora of sensors, cameras, microphones and other gadgets.
Now, they not only know whether all systems are operational, what our destination is or the speed limits ahead, but can also track the road conditions and the traffic around us to keep us safe. And lately, they have even started suggesting a coffee break in a well-rated coffee shop along the way when we show signs of drowsiness.
How do they know all this?
Well, it’s all thanks to knowledge graphs.
A knowledge graph is a data model that uses semantics to represent real-world entities and the relationships between them. It can apply automated reasoning to extract further knowledge and make new connections between different pieces of data. This model is used in various industries to enable seamless data integration, unification, analysis and sharing.
Going back to our example of a smart vehicle, what we talked about is only a small part of what knowledge graphs can do in the automotive industry. More and more companies are using them to improve a variety of tasks from product range specification and risk analysis to supporting self-driving cars.
Knowledge graphs can also enable the creation of “digital twins”, which make sense of the collected data from various sensors in different systems, spanning the entire vehicle lifecycle. This allows companies to model and optimize the interactions between the various computers that make a car run, ensuring everything is operating in sync to meet the desired specifications.
For some time, the manufacturing industry has been benefiting significantly from knowledge graph technology. Providing insights into the performance of different systems and processes, knowledge graphs can significantly improve asset management, equipment maintenance, factory floor management & optimization, industrial safety and a lot more.
As we have seen, many leading auto part makers and car manufacturers use knowledge graphs to improve their operations. But we are also happy to report that some of the biggest car manufacturers leverage Ontotext’s RDF database for knowledge graphs GraphDB in their technology.
And that’s not all. Some of the top U.S. computer manufacturers are relying on knowledge graphs (and GraphDB) in their operations. GraphDB also powers the systems of leading electronic equipment makers as well as two of the largest HVAC manufacturers.
Additionally, many organizations and corporations are pushing for the adoption of Industry 4.0 and other technologies for digital transformation of manufacturing systems. There is an overwhelming amount of standardization efforts and reference initiatives, which double down on the benefit from the knowledge graph approach.
Understandably, knowledge graphs are becoming a crucial technology in the aerospace industry as well. While these companies employ many standard manufacturing processes and operations, aerospace parts manufacturing also requires advanced quality standards and significantly more administrative effort.
Here again knowledge graphs organize and link large amounts of data on aircraft design, manufacturing, maintenance and performance. By linking this data, they facilitate tasks like asset management, predictive maintenance, documentation management, mission planning, risk management, aircraft design and optimization, and anomaly detection.
Since 2020, Ontotext has been working with NASA and, interestingly, six out of the top ten aerospace companies in the world use GraphDB in some part of their operations.
Two other industries where knowledge graphs are hitting the mark are logistics and supply chain management. In logistics, knowledge graphs can optimize operations by improving asset tracking, managing fleets, performing precise shipment tracking, assisting the optimal loading of vehicles as well as space utilization in warehouses and much more.
In supply chain management there is also a lot of semantic work and standardization that is being done. Organizations such as GS1 promote the use of standards such as barcodes, GS1 digital link and the GS1 vocabulary. Another important standard in terms of logistics and supply chain management is EPCIS, which is enabled by IoT technologies and provides real-time tracking information. This delivers greater visibility to product movement and tracking events throughout the whole supply chain, for fostering interoperability and transparency.
Knowledge graphs are also making waves in retail, helping companies create better product catalogs, search engines, recommendation systems, global loyalty programs, enterprise data management, computer vision applications and more. The possibilities are endless!
Not surprisingly, one of the largest UK retailer companies is using GraphDB for their recommendation systems. GraphDB also powers the cognitive search of a top Japanese retailer and is essential in the development of product catalogs for a leading Swedish furniture retailer.
As you can see, there are many industries where knowledge graphs are revolutionizing the way businesses operate. But the benefits of knowledge graphs don’t stop there. Their real power lies in the ability to integrate and connect data not only across systems, but also industries.
Let’s have a look at a few examples.
Knowledge graphs can link data from customer loyalty programs, inventory management systems and supply chain management. This allows companies to gain a more complete view of their customers, inventory levels and supply chain operations. This can then lead to better recommendations, more accurate inventory management, improved supply chain efficiency and ultimately improvement of sales numbers.
As knowledge graphs can represent the relationships between different entities, such as vehicles, cargo and warehouses, they can identify patterns and dependencies between the automotive industry and logistics that are not immediately obvious from the raw data. This can support the development of advanced logistics strategies where unforeseen risks and disruptions are taken into consideration for mitigation and optimization efforts.
By linking data from car sensors and cameras to data from traffic signals, weather forecasts, bus schedules and social media, knowledge graphs can provide a unified view of a city’s transportation system. This can help improve its efficiency, safety and sustainability and enable smart city applications such as traffic management and route optimization.
Knowledge graphs can also aggregate data from various manufacturing systems for tracking production processes together with data from smart power grids. As a result, manufacturers can optimize their production strategy, operation and maintenance, working environment, waste and resource management, etc.
Retailers also worry about energy cost as well as storage capacity, space utilization, seasonal temperature demands, fresh air, etc. Here knowledge graphs can help by connecting storage data from different sources to data from various building automation processes. This can lead to operational cost cutting and improve competitiveness.
Knowledge graphs play a vital role in connecting the data from siloed legacy systems and platforms, enabling seamless data sharing, knowledge discovery and analytics. On top of that, they are a powerful tool that can bridge the gap between different industries by further connecting and integrating data across systems.
By using knowledge graphs, companies can gain a more holistic view of their operations, make better-informed decisions and optimize their processes. This is particularly useful in today’s data-driven economy, where having a complete picture of the data can give enterprises a competitive edge.
Want to learn more about how knowledge graphs can help your business?