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Welcome to the Big Textual Data Analytics and Knowledge Management MCQs Page

Dive deep into the fascinating world of Big Textual Data Analytics and Knowledge Management with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Textual Data Analytics and Knowledge Management, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Textual Data Analytics and Knowledge Management, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Check out the MCQs below to embark on an enriching journey through Big Textual Data Analytics and Knowledge Management. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Textual Data Analytics and Knowledge Management. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Textual Data Analytics and Knowledge Management MCQs | Page 11 of 11

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Q101.
What is a significant impact of unstructured information growth on knowledge life cycles?

a.

A decrease in unstructured information relative to structured information

b.

A reduction in the need for information retrieval and extraction methods

c.

An increase in unstructured information compared to structured information

d.

A lower demand for knowledge broadcasting

Discuss
Answer: (c).An increase in unstructured information compared to structured information Explanation:The relative amount of unstructured information is increasing fast compared to structured information.
Q102.
How can semantic technology be applied in analyzing web pages and texts for tracing entity descriptions and changes over time?

a.

By directly increasing structured information production

b.

By supporting knowledge production life cycles

c.

By reducing the amount of unstructured information

d.

By minimizing the use of information retrieval methods

Discuss
Answer: (b).By supporting knowledge production life cycles Explanation:Semantic technology can be applied to trace entity descriptions and changes over time, supporting knowledge production life cycles by codifying knowledge claims.
Q103.
What benefit does the production of structured information through information extraction methods offer in knowledge integration?

a.

Improved search engine capabilities

b.

Enhanced knowledge broadcasting

c.

Streamlined content syndication

d.

Facilitation of knowledge distribution to subscribers

Discuss
Answer: (b).Enhanced knowledge broadcasting Explanation:The production of structured information through information extraction methods offers benefits, especially for knowledge broadcasting - distributing knowledge to subscribers involved in various knowledge-related activities.
Q104.
How does the integration of natural language processing technologies with knowledge lifecycle optimization and knowledge management benefit organizations?

a.

By reducing the need for information extraction methods

b.

By decreasing usability ratings among knowledge workers

c.

By enabling new, broadly usable organizational knowledge management support tools

d.

By eliminating the demand for content syndication mechanisms

Discuss
Answer: (c).By enabling new, broadly usable organizational knowledge management support tools Explanation:The integration of natural language processing technologies with knowledge lifecycle optimization and knowledge management enables a new generation of broadly usable organizational knowledge management support tools.
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