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Welcome to the Linked Data in Enterprise Integration MCQs Page

Dive deep into the fascinating world of Linked Data in Enterprise Integration with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Linked Data in Enterprise Integration, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Linked Data in Enterprise Integration, 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 Linked Data in Enterprise Integration. 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 Linked Data in Enterprise Integration. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Linked Data in Enterprise Integration MCQs | Page 10 of 11

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Q91.
How is the score of rules <x โ†’ ฮต> adjusted in CaRLA?

a.

It is increased by a factor ฮบ

b.

It is decreased by a factor ฮบ

c.

It is multiplied by a factor ฯƒ

d.

It is multiplied by a factor ฮต

Discuss
Answer: (b).It is decreased by a factor ฮบ Explanation:In CaRLA, the score of rules <x โ†’ ฮต> is decreased by a factor ฮบ to ensure that the computation does not favor deletions.
Q92.
How does CaRLA handle ties when choosing between rules <x โ†’ y> and <x โ†’ yโ€ฒ>?

a.

It selects <x โ†’ y> if ฯƒ(x, y) > ฯƒ(x, yโ€ฒ)

b.

It selects <x โ†’ yโ€ฒ> if ฯƒ(x, y) > ฯƒ(x, yโ€ฒ)

c.

It selects both <x โ†’ y> and <x โ†’ yโ€ฒ>

d.

It randomly selects <x โ†’ y> or <x โ†’ yโ€ฒ>

Discuss
Answer: (a).It selects <x โ†’ y> if ฯƒ(x, y) > ฯƒ(x, yโ€ฒ) Explanation:CaRLA selects rule <x โ†’ y> if the similarity between x and y is greater than the similarity between x and yโ€ฒ.
Q93.
What is the final score function used in CaRLA?

a.

scorefinal(<x โ†’ ฮต>) = 0

b.

scorefinal(<x โ†’ y>) = score(<x โ†’ y>)

c.

scorefinal(<x โ†’ ฮต>) = ฮบ

d.

scorefinal(<x โ†’ y>) = score(<x โ†’ y>) + ฯƒ(x, y)

Discuss
Answer: (d).scorefinal(<x โ†’ y>) = score(<x โ†’ y>) + ฯƒ(x, y) Explanation:The final score function in CaRLA is computed as scorefinal(<x โ†’ y>) = score(<x โ†’ y>) + ฯƒ(x, y).
Q94.
What does CaRLA do in the rule merging step?

a.

It computes the Jaccard similarity

b.

It discards rules <x โ†’ y> if <y โ†’ x> is in the other set of rules

c.

It tokenizes property values

d.

It computes the transformation function ฯ†R

Discuss
Answer: (b).It discards rules <x โ†’ y> if <y โ†’ x> is in the other set of rules Explanation:In the rule merging step of CaRLA, it discards rules <x โ†’ y> if <y โ†’ x> is in the other set of rules.
Q95.
How are low-weight rules handled in CaRLA during rule merging and filtering?

a.

They are always discarded

b.

They are always kept

c.

They are multiplied by a factor ฮบ

d.

They are multiplied by a factor ฯƒ

Discuss
Answer: (a).They are always discarded Explanation:In CaRLA, low-weight rules are discarded during rule merging and filtering.
Q96.
What determines the initial similarity threshold ฮธ in CaRLA?

a.

The size of set P

b.

The size of set N

c.

The Jaccard similarity

d.

The tokenization function

Discuss
Answer: (a).The size of set P Explanation:The initial similarity threshold ฮธ in CaRLA is determined by the size of set P.
Q97.
How does CaRLA compute the final value of ฮธ?

a.

By taking the minimum of the similarity thresholds of both sets of rules

b.

By taking the maximum of the similarity thresholds of both sets of rules

c.

By averaging the similarity thresholds of both sets of rules

d.

By summing the similarity thresholds of both sets of rules

Discuss
Answer: (a).By taking the minimum of the similarity thresholds of both sets of rules Explanation:CaRLA computes the final value of ฮธ by taking the minimum of the similarity thresholds of both sets of rules.
Q98.
What does the output of CaRLA consist of?

a.

The initial similarity threshold

b.

The tokenization function

c.

The transformation function ฯ†R

d.

The retrieved set of rules and the value of ฮธ

Discuss
Answer: (d).The retrieved set of rules and the value of ฮธ Explanation:The output of CaRLA consists of the retrieved set of rules and the value of ฮธ.
Q99.
What is the goal of the rule falsification step in CaRLA?

a.

To compute the transformation functions ฯ†R1 and ฯ†R2

b.

To detect a set of transformations that lead to a maximal number of elements of N having a similarity superior to ฮธ

c.

To tokenize property values

d.

To compute the initial similarity threshold

Discuss
Answer: (b).To detect a set of transformations that lead to a maximal number of elements of N having a similarity superior to ฮธ Explanation:The goal of the rule falsification step in CaRLA is to detect a set of transformations that leads to a minimal number of elements of N (negative training examples) having a similarity superior to the threshold ฮธ via the similarity function ฯƒ.
Q100.
How does CaRLA handle the rule falsification process?

a.

It tries to validate all rules simultaneously

b.

It tries to refute or validate rules with a score below the score threshold smin

c.

It discards all rules that apply to elements of E by descending score

d.

It discards all rules with a score above the score threshold smin

Discuss
Answer: (b).It tries to refute or validate rules with a score below the score threshold smin Explanation:CaRLA handles the rule falsification process by trying to refute or validate rules with a score below the score threshold smin. It does this iteratively by picking the most unsure rule and fetching a set of property values that map the left side (premise) of the rule. It then requests annotations for these property values to determine if the rule is valid or should be discarded.
Page 10 of 11

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