Integrating updating domain knowledge data mining


12-Aug-2020 02:28

integrating updating domain knowledge data mining-5

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Even if a researcher has only the basic skills in programming, he or she will be able to make a deep research using these libraries.

Text mining contains the following major steps: data collection and preprocessing, identification of entities and their links, and knowledge representation. For example, one can require permission to get data from a database or publisher and can also retrieve data form the Web by a data extractor.

The obtained data from different sources may be recoded in diverse formats, such as text files and scanned images.

These knowledge graphs devote to acquire entities and their links for various topics during the construction.Elsevier and Springer have provided application programming interfaces (API) for developer and scientists to access metadata, full text, and conduct text mining [10, 11].We anticipate that more geological literature will be made available by publishers, government agencies, research organizations, and individual scientists in the coming years.The new development in interpreted programming language and the wide-spreading open-source packages and libraries enable scholars in various disciplines to quickly learn the latest algorithms and apply them to their domain-specific researches.

There are many widely used open and free libraries in text mining, such as Tensor Flow [13], Deep Dive [14], Caffe [15], CNTK [16], and MXnet [17].

The diverse big data and improved computer software and hardware enable an opportunity to understand the evolution of Earth system using simulation and data mining methods [6].



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