Read about how knowledge graphs impact different industries by connecting and integrating data for improved knowledge discovery, analysis and decision making
In our previous blog post, Bridging the Gap Between Industries: The Power of Knowledge Graphs – part I, we talked about starting the day with our smart car looking out for us, powered by knowledge graph technology. We also looked at how knowledge graphs are changing the game in Manufacturing, Aerospace, Logistics, Supply Chain and Retail and how they can enable cross-industry interoperability.
Now let’s continue our day! Let’s imagine that, after getting a second cup of coffee (at the suggestion of our smart car), we go to the office.
The building where our offices were located before was always hot and stuffy in the summer and cold and humid in the winter although the air conditioning worked full time. But now that we’ve moved to a new smart building, the temperature automatically adjusts depending on the season, the time of day and the number of people in the building. The brightness of the lights is also automatically regulated, based on how much natural light is coming in from outside, if there are people working late or not, etc.
This is all thanks to Building Automation, which enables the integration of different systems and uses data and automation to optimize a building’s performance, increase the occupants’ comfort and reduce operating costs.
Building Automation is another sector that has started to rely heavily on knowledge graphs for its operations. Thanks to their ability to seamlessly integrate disparate data pieces, knowledge graphs can connect information from different building systems such as heating, ventilation & air conditioning (HVAC), lighting, security, fire safety, elevators, power management, etc.
The resulting comprehensive view of the building’s performance can lead to more informed decisions. For example, based on statistical models, a building can anticipate the increased presence of people and adjust temperature, ventilation and lighting, optimize elevator schedules, etc. Or power generation can be shifted from selling to the smart grid to buying from it, depending on the needs of the building’s occupants.
One of the popular ontologies that are used to model the different components of a smart building is BrickSchema. And, as more and more Building Automation providers are turning to knowledge graphs, two of the leading ones – Johnson Controls and Schneider Electric – have chosen Ontotext’s GraphDB. They are using GraphDB with BrickSchema to leverage semantic technologies in their building automation solutions. Thanks to its scalability, GraphDB is an excellent choice for OEMs and it’s implemented already in thousands of buildings around the world.
But a smart building is truly a building that is designed to be smart from its conception. Not surprisingly, AECO (Architecture, Engineering, Construction and Operations) has also been reaping the benefits of knowledge graphs for a while now. By linking data from building designs to information about materials used, energy efficiency, elevator speed, capacity and more, construction companies can now further optimize their Building Information Management systems.
But knowledge graphs can transform more than the different aspects of a construction process. They can also aid with regulation compliance, construction resource planning, linking with Mapping Agencies and Land Registries, building monitoring, maintenance, security and many other tasks. Thanks to the dynamic nature of knowledge graphs, companies can add additional layers of complexity to address their changing business demands.
One more area of knowledge graph application in AECO is digital building log books. These serve as the passport of a building starting from its planning, building and operating to its decommissioning. In Switzerland knowledge graphs are also used in drone inspections for bridges and other types of infrastructure, while in Norway they facilitate road maintenance.
Another industry closely related to AECO and Building Automation is the electric power industry. If we think about it, in its essence, an energy grid is a graph. It consists of various components such as power stations, substations, generators, transformers, power lines, etc., which makes it easy to use a semantic approach for representing them. That is why energy grid modeling based on a knowledge graph is used for a variety of tasks. These include capacity planning, peak performance requirements, distribution management, network model exchanges, outage management systems and many others.
There are some European regulations that mandate the use of this approach, and more and more companies are starting to apply it. Statnett in Norway have their energy grid network in RDF and can share it across borders, HOPS in Croatia and EDF in France are also using semantics to improve their operations.
Another closely related sector is the water industry, where knowledge graphs can help again by providing a way to link and organize vast amounts of data from various sources such as water quality, flow rates, pressure, temperature, etc.
For example, they can improve the management of water infrastructure by linking data on the age, condition and location of pipelines, treatment plants and other assets with information on maintenance schedules, service records and repair history. They can also enhance demand management, flood protection, environment protection and many more.
Some research has already been done in this field, although it is not yet as widespread as in the electric power industry.
We’ve already talked about Building Automation, AECO, electric power & water systems. But what happens if we put these together and connect them to other systems like air quality control, public transport, parking, municipal services, event management, public security, healthcare, etc.? The answer is easy: a smart city.
Smart cities are considered ‘smart’ because they make use of technology and data to manage assets, resources and services more efficiently. This data can then be used for improving city operations such as monitoring traffic and transportation systems, water supply, schools, libraries, hospitals and other community services. The data can also be shared with citizens and businesses to create opportunities for increased awareness and economic benefit.
Utilizing knowledge graphs in a smart city can help in a variety of tasks such as identifying areas with high levels of pollution or making a decision where to place a new hospital. Also, using data from traffic cameras and information on road conditions, city officials can analyze traffic patterns and optimize traffic flow in real-time. Or by linking data from schools, libraries, hospitals and other community services, they can monitor and improve the delivery of public services.
There are many smart city initiatives around the world such as Vienna, Sofia, Singapore and New York. As cities continue to grow, it becomes increasingly important for them to adopt smart city technologies in order to improve the quality of life for citizens and optimize the use of resources.
As we already said in our previous post, there are many industries where knowledge graphs are revolutionizing the way businesses operate but their real power lies in the ability to connect and integrate data across different industries.
Let’s have a look at some more examples.
Knowledge graphs can connect data from the design and construction of a building to data from various building automation systems to identify areas for improvement. For example, data on building materials, energy consumption and occupancy patterns can be interlinked to provide a comprehensive view of the building’s performance and help analyze anticipated vs actual performance.
Data on energy consumption from a smart building can be interconnected with data on power grid conditions, generation and demand to optimize the energy usage in the building. This can also aid with demand management on the power grid and the more efficient use of energy resources. It also unlocks new business models and incentives.
Knowledge graphs can link data from water systems in various ways as well. For example, data on water usage and leak detection can be linked to data on HVAC and irrigation systems, helping with optimizing usage and identifying potential issues.
Also, by integrating data on customer billing, service requests and usage history, water utilities can create a more personalized experience for their customers. Or by linking data on water quality and energy consumption with information on land use, development officials in smart cities can better plan for changes in demand and adjust their operations accordingly.
Knowledge graphs have proven to be a versatile and essential technology for smart architecture, engineering, construction & operations, smart buildings, smart power grids and water systems, smart cities and many other businesses. They are powerful tools that enable the representation and organization of complex data in a way that is easily accessible and understandable by both humans and machines.
By providing a unified framework for data management and analysis, they provide valuable insights and facilitate data exchange between many industries. This leads to increased efficiency, better decision-making and improved outcomes. As the world continues to become more connected and data-driven, knowledge graphs will play a crucial role in bringing different industries and enabling greater collaboration.
Want to learn more about how knowledge graphs can help your business?