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

Ontotext

Going back to our example of a smart vehicle, what we talked about is only a small part of what knowledge graphs can do in the automotive industry. 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.

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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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Unlocking the Power of Better Data Science Workflows

Smart Data Collective

Phase 4: Knowledge Discovery. When these two elements are in harmony, there are fewer delays and less risk of data corruption. Phase 3: Data Visualization. With the data analyzed and stored in spreadsheets, it’s time to visualize the data so that it can be presented in an effective and persuasive manner.

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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? Economy.bg: The pros in this respect are indisputable. How to prepare for a future without employment? Milena Yankova : Will AI replace us? It’s very likely.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

Medicine uses the term “relative risk” to describe effect fraction when referring to the fractional change in incidence of some (bad) outcome like mortality or disease. As noted earlier, effect fractions of 1% or 2% can have practical significance to an LSOS.

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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. The company can lower the risk value of the red line and monitor the situation in real time. Data Visualization. How BI system solve the problem?

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining.