Unlock the power of Data Science with Ontologies

In AI, teaching a machine to recognize targets across myriad characteristics and contexts is much like teaching a toddler what a rabbit is. Ontologies are not merely object classifiers like taxonomies and species. They are more abundant in dimensions and can define structure through a mixture of objects, events, properties, and more. Ontologies provide contexts, which are richer than the typical natural language processors and classifications. They can be leveraged as learning accelerators for machines, allowing them to iterate faster while picking up more subtleties.

With seed Ontologies as the initial set of linkages in a learning network, a “Knowledge Graph” evolves, showing the relationships among entities or data points in graphical form. The deeper the network becomes, the more relationships among data points are identified, hence the faster one can search through the data. Knowledge Graphs are among the most promising representations today in scaling big, real time data and generating insights quickly.

Knowledge Graph: Do more by joining the dots

Ontologies and Knowledge Graphs are particularly useful in mining enterprise data because of the linkages they find among data points. For example, while traditional methods would consider shopper information, grocery lists, and card transactions as three separate data sets, a Knowledge Graph, with its self-learning Ontologies, is able to search for patterns across all three data sets simultaneously and instantaneously. It is more than a parallel search; it is contextual search. While Machine Learning can be deployed on one data set at a time, the sum is much greater than its parts. Knowledge Graph-based Machine Learning has the elegance of how a human brain would actually go about “learning” something.

Case Studies

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