Robust information architecture is needed to support sophisticated solutions that are emerging as enterprises move forward with their digital transformations. Ontologies provide the re-usable, adaptive structure for enterprises that want to power their Artificial Intelligence (AI) initiatives. There are two approaches enterprises can take to power AI. One is the machine learning approach and the other is through ontologies.
Robust information architecture is needed to support sophisticated solutions that are emerging as enterprises move forward with their digital transformations. Ontologies provide the re-usable, adaptive structure for enterprises that want to power their Artificial Intelligence (AI) initiatives. There are two approaches enterprises can take to power AI. One is the machine learning approach and the other is through ontologies.
Ontologies based approach to AI emulates human performance by allowing the system to make inferences based on content and relationships. Besides, it can produce extremely targeted results and unlike the machine learning-based approach does not require the use of training sets to become functional.
Beginning with controlled vocabulary at the simplest level, knowledge models are a continuous sequence in which adjacent levels have no perceptible differences from each other, but the extremes are quite distinct. In this continuum, ontologies are the relationships among multiple taxonomies, and specific instances of the relationship can be captured using knowledge graphs.
Ontology is the knowledge scaffolding in an enterprise, and maximum value can be derived by using that structure across multiple systems and data sources. Furthermore, ontologies help in gaining knowledge of relationships between concepts. This allows an enterprise to find solutions to problems or match products or services with another set of products or services.
An ontology allows a system to infer conclusions and answer unanticipated questions that have not been programmed into them. It also allows systems to communicate with each other, thereby, providing a richer context and knowledge base. Ontologies can work across multiple systems and help enterprises reuse existing knowledge and resources by breaking them up into components. Subsequently, the components can be fed into multiple systems and applications and sent to multiple user-selected interfaces.
For a full digital transformation, an enterprise will need hundreds or thousands of AI projects, so it is essential to set the stage for scaling up by having a repeatable framework. With an ontology in place, changes can be made to the data in one location and propagated through the existing associative relationships. So, if there is a change in the price of a service, for example, the system does not require re-coding in multiple applications; the data can be changed once and carried through to all of them.
Ontologies provide the robust re-usable adaptive information architecture needed to support sophisticated solutions that are emerging as enterprises move forward with their digital transformations. Significantly, the more detailed the ontology, the more meaningful will be the responses that users receive.
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