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.

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 5 of 11

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Q41.

What modeling focus is distinguished for information retrieval?

Entity focus

Term focus

Fact-based focus

Relationship focus

Discuss

Answer: (b).Term focus Explanation:For information retrieval, the modeling focus distinguished is the term focus.

Q42.

How does document classification help in the context of electronic patent filing?

It helps assign patent authors to patent applications.

It helps identify common taxonomies.

It assists in near-automatic assignment of reviewers to patent applications.

It provides keywords for patent authors.

Discuss

Answer: (c).It assists in near-automatic assignment of reviewers to patent applications. Explanation:In the context of electronic patent filing, document classification assists in near-automatic assignment of reviewers to patent applications.

Q43.

In knowledge discovery procedures, what is the term "multilabeling" typically associated with?

Fact-based analysis

Shallow NLP

Document classification

Relationship extraction

Discuss

Answer: (c).Document classification Explanation:In knowledge discovery procedures, the term "multilabeling" is typically associated with document classification.

Q44.

In practice, what are the two machine learning method approaches often combined for in an exploratory phase?

Classification and information retrieval

Clustering and information extraction

Shallow NLP and deep NLP

Clustering and class identification

Discuss

Answer: (b).Clustering and information extraction Explanation:In practice, the two machine learning method approaches are often combined for class identification and a detailed modeling phase based on training a classifier on the classes.

Q45.

What is the primary purpose of document-based information extraction?

To analyze the sentiment in documents

To generate document term profiles

To uncover specific items of information according to a predefined information structure

To perform multidocument similarity analysis

Discuss

Answer: (c).To uncover specific items of information according to a predefined information structure Explanation:The primary purpose of document-based information extraction is to uncover specific items of information according to a predefined information structure.

Q46.

How is document classification based on information extraction used in opinion or sentiment mining?

To identify possible entities or relationships

To generate document term profiles

To detect statements in documents

To examine the degree of concepts related to opinions

Discuss

Answer: (d).To examine the degree of concepts related to opinions Explanation:Document classification based on information extraction is used in opinion or sentiment mining to examine the degree of concepts related to opinions.

Q47.

What is the main distinction between knowledge extraction and information extraction?

Knowledge extraction involves ontology-based text annotation technology.

Information extraction is based on unsupervised methods.

Knowledge extraction uses supervised methods to populate ontologies.

Information extraction focuses on conceptual analysis.

Discuss

Answer: (c).Knowledge extraction uses supervised methods to populate ontologies. Explanation:The main distinction between knowledge extraction and information extraction is that knowledge extraction uses supervised methods to populate ontologies.

Q48.

What is the purpose of unsupervised methods for information extraction on the corpus level?

To analyze sentiment in documents

To perform multidocument similarity analysis

To generate document term profiles

To extract plausible ontology concepts and predicates

Discuss

Answer: (d).To extract plausible ontology concepts and predicates Explanation:The purpose of unsupervised methods for information extraction on the corpus level is to extract plausible ontology concepts and predicates, particularly related to ontology learning.

Q49.

In multidocument analysis, how is document similarity typically measured?

By using cosine similarity measure

By employing deep NLP techniques

By calculating term frequency-inverse document frequency (TF-IDF)

By conducting sentiment analysis

Discuss

Answer: (a).By using cosine similarity measure Explanation:In multidocument analysis, document similarity is typically measured using the cosine similarity measure.

Q50.

How are entities and their relationships treated in the modeling level of information extraction?

As individual sentences within a document

As separate XML schema elements

As bags of words

As mentions and their possible relationships to other entities

Discuss

Answer: (d).As mentions and their possible relationships to other entities Explanation:In the modeling level of information extraction, entities and their relationships are treated as mentions and their possible relationships to other entities.

Page 5 of 11

Introduction to Big Data 🔒

Management of Big Semantic Data 🔒

Linked Data in Enterprise Integration 🔒

Scalable End User Access to Big Data 🔒

Big Data Exploration 🔒

Big Data Processing with MapReduce 🔒

Advanced Data Analytics for Business 🔒

Real Time Big Data Processing 🔒

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

Big Textual Data Analytics and Knowledge Management MCQs | Page 5 of 11

Explore more Topics under Big Data Computing

Big Data Challenges and Opportunities

Semantic Data Interoperability

Efficient Processing of Stream Data over Persistent Data

Economics of Big Data

Big Social Data Analysis

Application of Big Data in Analyzing Electric Meter Data

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