Smart Solutions for Identifying Compatible Components - Powered by metaphactory and RDFox

Use Cases

Irina Schmidt, Felicity Mulford



Reading time: 4 - 8 minutes

Smart Solutions for Identifying Compatible Components

Determining compatibility between individual entities is an essential process for many businesses, across various industries and business models; from industrial configuration, supply chain, bill of materials, evaluating terms in contracts, or even for match making apps. The process may sometimes require the user to check hundreds of thousands or millions of possible combinations, to assess whether components fit together, or if components meet specified requirements. Additional factors may also need to be taken into account, for example, regulations or customer budgets. Traditional approaches are inefficient for modern day applications due to the large volumes of data, heterogeneity of data formats, complexity of customer specifications, and concerns over scalability.

Determining compatibility in configuration management

In industrial configuration scenarios, for example, users need to identify suitable solutions by looking at hundreds or thousands of technical parts, each with their own set of attributes and constraints. In this case, the process of assessing compatibility using a traditional system can take up to 15 hours and involve numerous data export and aggregation steps as well as cross-checks across multiple systems - and this just for one compatibility solution. This process can be error-prone and is inefficient when businesses are meeting real-time customer demands. Further challenges may arise if the system changes; in relational systems, for example, the whole database must reload, which is a timely process, and checks must be performed to ensure that the changes haven’t impacted the correctness or consistency of the application.

Why Knowledge Graphs?

Knowledge Graphs have structural advantages over traditional models for solving compatibility solutions. They overcome the flexibility limitations of relational databases as data is stored as richly connected entities, the system can be updated easily, and end users can undertake targeted search, discovery and exploration. Knowledge Graphs ensure data is FAIR - Findable, Accessible, Interoperable and Reusable.

metaphactory and RDFox in action for configuration management

Smart Solutions for Identifying Compatible Components To demonstrate the unrivalled benefits of knowledge graphs in compatibility assessment scenarios, we've recently teamed up with Oxford Semantic Technologies and have developed a knowledge graph-based application for configuration management on top of metaphactory, our knowledge graph management, visualisation and interaction platform, and RDFox, Oxford Semantic Technologies' knowledge graph and semantic reasoning engine.

Together, metaphactory and RDFox deliver unprecedented results in compatibility determination scenarios by allowing users to quickly and efficiently gain access to actionable and meaningful insights.

In this blog post we'll explore how support engineers, product managers, technical planners, or technical maintenance specialists can quickly evaluate hundreds of components such as motors, gears, switches, power supplies and controllers, and their individual characteristics using the application built on top of metaphactory and RDFox. Using the system, end users can determine how components fit together and how they can be combined to create solutions that solve very specific customer needs or maintenance requests, while staying within a predefined budget.

Compatible rotation solutions at your fingertips

Let's consider the example of a support engineer who is looking for a complete rotation solution for one of her customers. The solution should come with a particular brushless motor, a specific power supply, a minimum speed (50 rpm) and torque (500 Nm), but should stay within a certain budget (max. 129€).

Compatibility between the components represented in the knowledge graph is determined by reasoning (Datalog rules) applied by RDFox. RDFox also automatically computes the cost of each solution based on the individual costs of the components. Using an intuitive interface built with metaphactory, the engineer can explore available solutions by simply selecting the components that should be included and defining the constraints that should be respected.

Rotation solutions fitting specific customer requirements

Once she has found a few solutions that satisfy her needs, the engineer can proceed to look at each one in detail and decide on the one she wants to order for her customer.

Now let’s say our support engineer is assigned to replace a faulty DC motor at a customer site, but all other components that are part of the customer’s existing rotation solution should be kept. Using the configuration management application built with metaphactory and RDFox, she can start by looking at the various DC motors available and filter down to find the ones compatible with the technical setup the customer has in place.

Keyword-type search interface in metaphactory

For example, she might be looking for a DC motor with a minimum torque of 200 Nm and a provided speed between 2,000 and 4,000 rpm, but which should not go over a predefined budget of €20.

DC motors fitting specific customer requirements

After a quick search based on her parameters, our engineer can go on to further explore her search results, for example which other components the resulting DC motors are compatible with:

DC motors and their compatibilities

and which rotation solutions these DC motors are part of:

DC motors and the rotation solutions they are included in

She can also look at each DC motor's specifications in detail:

Knowledge Panel with additional information about one DC motor

or visually explore relationships between a DC motor and other components:

Visual exploration of relationships between a DC motor and other components

Using the visual exploration component integrated into the application, our end user can quickly build a diagram to show power supplies this DC motor is compatible with, as well as rotation solutions it is part of. The engineer can also find out at a glance which information was initially loaded into RDFox (e.g., the grey "type" relationship tells us that this information is core information) and which information was inferred based on the rules defined with RDFox (e.g., the red "compatibleWith" or "component" relationships tell us that this is inferred information).

Precise answers to specific questions

With its keyword interpretation engine and its intuitive visual graph exploration component, metaphactory allows end users to leverage core and inferred graph data in RDFox and perform targeted, natural-language queries that deliver instant results and can be explored further to discover previously unknown connections. In the example below, our engineer is searching power supplies compatible with a particular DC motor (DCMotor12) to set up a rotation solution for a customer. She starts by defining a customer budget of €80 for the complete solution.

Adjusting a customer's budget

Then, she searches for all power supplies compatible with DCMotor12. She can type her keywords in the search bar and the system immediately returns a visual graph with all matching components. Note how metaphactory's keyword interpretation engine understood that the keyword "powersupply" refers to the "DCPowerSupply" entities stored in the knowledge graph.

Natural language querying to find compatible components

She then explores the data in the system further to find rotation solutions that include these components. Note that two rotation solutions including two different power supplies are within budget, while a third one slightly exceeds the budget.

Automatic taggig of rotation solutions that exceed the budget

From here, our engineer can further explore relations in the graph and discover that two of the power supplies listed are also compatible with a brushless motor controller, which in turn is compatible with multiple brushless motors.

Discovery of previously unknown relations

Immediate access to the latest data

As is explained in our joint whitepaper, using RDFox, knowledge graph experts can easily import new data along with a set of compatibility rules and the system will automatically reflect the changes in the data, thus always giving end users access to the latest data. Similarly, data can be deleted or the ontology can be adjusted to reflect changes in relationships, and the system and the user experience created with metaphactory will update accordingly.

Often there is also a need for end users to be able to modify or augment the data in the knowledge graph. Using metaphactory's intuitive semantic forms, end users are able to seamlessly add new components to their catalogue:

End-user interface for adding new components to the Knowledge Graph

Similarly, end users can change the configuration of existing components and the updates will immediately be propagated throughout the entire user experience. Let's say, for example, that our support engineer from before is again looking for a rotation solution with specific parameters but within a predefined budget of €50. As depicted by the screenshot below, all of the rotation solutions in the system exceed the €50 budget.

A sample search for rotation solutions within a certain budget

After some negotiations with her supplier of DC motors, our engineer can adjust the price for the DC motor that should be included in the customer solution:

End-user interface for modifying components

This will result in multiple rotation solutions being updated accordingly:

Automatic update of the user interface based on the newest data

Solution highlights

The metaphactory-RDFox joint solution offers a smart, unique and flexible method for determining compatibility solutions. With ontologies and semantic reasoning, the integration of hierarchies and logic brings the intelligence layer closer to the data. The metaphactory platform enables the end user to extract intelligible insights from the complex data and to explore this data through custom-built dashboards, optimised for search, visualisation, interactive exploration and authoring. For the user, the result is an adaptable solution, which can react to real-time changes.

Incremental reasoning capabilities allow the addition or removal of information and the adjustment of interaction patterns with almost negligible iteration periods, a feature which is far more appropriate for modern-day applications than slower, traditional methods. The usability and speed of the joint solution is bolstered by the ability to determine compatibility solutions ahead of query time, storing the new solutions as relationships within the knowledge graph, resulting in improved query performance and user satisfaction.

The end-to-end solution provides the chance for organisations to optimise their compatibility process with advanced technologies, customised with their business domain knowledge and expertise.

Sounds interesting?

Make sure to download our joint whitepaper to learn more about this use case and how things work behind the scenes.

To get started with your own Knowledge Graph application, sign up for a free metaphactory trial today!

Irina Schmidt

Irina is an international marketing and communications expert with over 10 years of experience in the areas of product marketing, online and digital marketing, public relations and customer success. She loves working at the crossroads where technology and business meet and is passionate about targeted marketing solutions that resonate with customers and solve real-world problems.