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Bridging the Gap Between Industries: The Power of Knowledge Graphs – Part I

Ontotext

More and more companies are using them to improve a variety of tasks from product range specification and risk analysis to supporting self-driving cars. This allows companies to model and optimize the interactions between the various computers that make a car run, ensuring everything is operating in sync to meet the desired specifications.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions.

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AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

Ontotext

This is a knowledge that anyone can get, but it would take much longer than optimal. But still, is there a risk that AI could replace people at their workplace? Milena Yankova : If they decide to work in IT, I would advise them to better understand the value of the data that machines collect from their interactions with us.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

In each case, users engage with the service at will and the service makes available a rich set of possible interactions. But the fact that a service could have millions of users and billions of interactions gives rise to both big data and methods which are effective with big data.

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Business Intelligence System: Definition, Application & Practice

FineReport

It is a process of using knowledge discovery tools to mine previously unknown and potentially useful knowledge. It is an active method of automatic discovery. When the amount of data onto an enterprise is getting larger, the data analysis requires deeper insights and interactivity. Data Visualization.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. Ribeiro, M.

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