How to Build an Ontology

Algorithmic BrAInAI Insights - The AI Imperative: How to Build an Ontology

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How to Build an Ontology

As you might expect, building an ontology that will make sense of all the data in your firm is a not only a difficult task but a Herculean one. Here are the crucial phases, based on the advisory work we have undertaken to go through the procedure:

  • 1. Determine data pain points first.

    Examine any areas where information issues and bottlenecks are hindering corporate functionality and then look for the underlying causality issues. This clarifies how resolving the larger data architecture issue might result in significant business advantages, even without AI.

  • 2. Develop solutions based on root causes.

    Start imagining what a potential solution would look and start with improving the processes that will have the biggest impact on the organisation if they are carried out properly.

  • 3. Comprehend the use cases.

    Think about who will benefit from each solution: customer service representatives, product engineers, users of an e-commerce website, etc. and precisely what duties it will enable them to complete more quickly or more consistently. Develop a model of what the person has to accomplish to attain their desired result, then test it out on real employees performing their duties. Find out how they would define the perfect solution, and then compare how they would describe it to how other employees in different jobs would describe challenges of a similar nature. These solutions are unified by the ontology, which serves as a representation of what counts within the organisation and what makes it distinctive, making them so much more than simple quick fixes that will soon become obsolete.

  • 4. Establish the guiding principles of the ontology.

    This is the ontology’s core concept. You can start to plan out the specifics of how to arrange and categorise the data you have so that it becomes a part of the solutions you are developing after you have a good understanding of how people are utilising the information. It becomes a crucial component of the answer to the information issue of today, as well as the problems that come after that. Remember that the ontology is a living, breathing thing that is always changing as goods, services, markets, rivals, and consumer demands change themselves. A purposeful process of updating the ontology will keep it current and useful. It’s becoming more and more obvious that AI will be handling many of these issues in the future.

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