If the world’s big data is a virtual mountain of dots, how can you connect them to extract their value? Knowledge graphs (KGs) will help. The world’s giant tech companies jumped on the “graph train” a while ago and now power some of the best-known tools, platforms, and services through graphs: the World Wide Web, social media, Web stores, and search engines. However, this does not mean companies should immediately start replacing all relational databases with graphs. New AI technologies tend to be initially seen as miracle solutions that will solve most problems (e.g., deep learning).
When deciding whether to use only graphs, only relational databases, or a combination, make sure to ask some key questions. For example, how important is rapid data exploration? How crucial is the speed at which data can be added to, and/or retrieved from, the data store? If graphs still come out as highly viable candidates, here are a few advantages you can expect from using them in your next data-driven project.
Visualize Your Data to Unlock New Insights
Because graphs are so easy to visualize, it takes little effort to find all the information associated with a node and the direct/indirect relations that link two nodes. This property of KGs both simplifies data exploration and provides richer insights into it. The multiple dimensions of the KG can easily be explored by slicing it across one or more dimensions. In a use case, visualizing the software requirement specifications (SRSs)-software component specifications (SCS) architecture led to a key hypothesis of the problem: the way specifications are linked and clustered together is closely related to the vertical traceability analysis outcome. An SRS connected to a failed SRS through nearby elements is more likely to be flagged as a FAIL.
Extract More from Your Data
The inherent interdependencies hidden in your data can be brought to light and leveraged by running algorithms on your graph. As seen in a previous use case, they can be used to compute several metrics on the whole graph or a sub-portion that can help make sense of your connected data and their inner workings. These metrics can then be used as features to power a machine learning model.
Start Small, Scale Fast
You might initially decide to build a graph that models a small portion of your domain space. That’s fine. Nothing prevents you from later expanding it to answer new questions or because more data becomes available.
Within graphs, it is easy to add a new type of node property or relationship. That is, the new property/relationship can be applied to a (potentially small) subset of nodes. If you have many node properties and/or relationships that apply only locally, KGs will be both much smaller and faster to process than their corresponding relational databases. Multiple graphs can also be combined if they share or have related entities, limiting high rearchitecting costs and enabling you to quickly grow your solution.
[For more from the authors on this topic. see: “Knowledge Graphs in Engineering: A New Perspective.”]
