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New Approaches in Knowledge Graphs boost Potential of AI Analytics

The amount of data we receive and generate in the era of internet is enormous and artificial intelligence plays a pivotal role in them.

After spending a lot of time in research and development to find the best way to organize and store data, relational data management was introduced in early 1970’s. However, as the volume of data keeps increasing, sticking to this method of columns and rows is difficult to continue with.

Tech Giants capitalizing on Potential of Knowledge Graph

Knowledge graph is gaining popularity due to properties such as ability to function with interrelated and complex data. In other terms, it stores information or data in a graphical format. It also can generate graphical relationship between any given data.

This reduces the time and energy that goes into computing despite the fact that data can fit into given table.

Currently, Oracle is the leading company in data analytics field which makes knowledge graph accessible to range of business applications. A Senior Director of Oracle, Hassan Chaffi, said that it worked in a tabular form with relational data.

Tech giants dealing with huge amounts of complex data are end consumers of knowledge graph. A case in point is Netflix using it for making a connection between movies and TV shows, and organize data in an accurate manner.

Siemens, the electronics giant, also uses knowledge graph to create accessible models for that data that it generates. The same is used for process monitoring, risk management, and building prototyping and training.

On the other hand, machine learning can give impressive results in areas like healthcare and law due to vast potential it has.

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