Semantic Network is also known as Frame networks.

a) True

b) False

1 Answer

Answer :

a) True

Related questions

Description : There exists two way to infer using semantic networks in which knowledge is represented as Frames. 1) Intersection Search 2) Inheritance Search a) True b) False

Last Answer : 1) Intersection Search

Description : Like semantic networks, frames can be queried using spreading activation. a) True b) False

Last Answer : a) True

Description : Most semantic networks are not cognitive based. a) True b) False

Last Answer : b) False

Description : Which of the following is an extension of the semantic network? a) Expert Systems b) Rule Based Expert Systems c) Decision Tree Based networks d) Partitioned Networks

Last Answer : d) Partitioned Networks

Description : The basic inference mechanism in semantic network in which knowledge is represented as Frames is to follow the links between the nodes. a) True b) False

Last Answer : a) True

Description : A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another. a) True b) False

Last Answer : a) True

Description : The basic inference mechanism in semantic network is to follow the links between the nodes. a) True b) False

Last Answer : a) True

Description : What are the limitations of the semantic networks? a) Intractability b) Lack in expressing some of the properties c) Incomplete d) Has memory constraints

Last Answer : b) Lack in expressing some of the properties

Description : Which of the following are the Semantic Relations used in Semantic Networks? a) Meronymy b) Holonymy c) Hyponymy d) All of the mentioned

Last Answer : d) All of the mentioned

Description : What are Semantic Networks? a) A way of representing knowledge b) Data Structure c) Data Type d) None of the mentioned

Last Answer : a) A way of representing knowledge

Description : Frames in artificial intelligence is derived from semantic nets. a) True b) False

Last Answer : a) True

Description : Graph used to represent semantic network is _____________ a) Undirected graph b) Directed graph c) Directed Acyclic graph (DAG) d) Directed complete graph

Last Answer : b) Directed graph

Description : Neurons or artificial neurons have the capability to model networks of original neurons as found in brain A. True B. False

Last Answer : A. True

Description : Which of the following is true for neural networks? (i) The training time depends on the size of the network. (ii) Neural networks can be simulated on a conventional computer. (iii) Artificial neurons are identical in ... b) (ii) is true c) (i) and (ii) are true d) None of the mentioned

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Description : Many words have more than one meaning; we have to select the meaning which makes the most sense in context. This can be resolved by ____________ a) Fuzzy Logic b) Word Sense Disambiguation c) Shallow Semantic Analysis d) All of the mentioned

Last Answer : b) Word Sense Disambiguation

Description : Semantic grammars are _____________ a) Encode semantic information into a syntactic grammar b) Decode semantic information into a syntactic grammar c) Encode syntactic information into a semantic grammar d) Decode syntactic information into a semantic grammar

Last Answer : a) Encode semantic information into a syntactic grammar

Description : The “Turing Machine” showed that you could use a/an _____ system to program any algorithmic task. a) binary b) electro-chemical c) recursive d) semantic

Last Answer : a) binary

Description : Where does the dependance of experience is reflected in prior probability sentences? a) Syntactic distinction b) Semantic distinction c) Both Syntactic & Semantic distinction d) None of the mentioned

Last Answer : a) Syntactic distinction

Description : What kind of interpretation is done by adding context-dependant information? a) Semantic b) Syntactic c) Pragmatic d) None of the mentioned

Last Answer : c) Pragmatic

Description : What can’t be done in the semantic interpretation? a) Logical term b) Complete logical sentence c) Both Logical term & Complete logical sentence d) None of the mentioned

Last Answer : c) Both Logical term & Complete logical sentence

Description : What is the process of associating a FOL expression with a phrase? a) Interpretation b) Augmented reality c) Semantic interpretation d) Augmented interpretation

Last Answer : c) Semantic interpretation

Description : What is the extraction of the meaning of utterance? a) Syntactic b) Semantic c) Pragmatic d) None of the mentioned

Last Answer : b) Semantic

Description : Define Semantic Interpretation.

Last Answer :  The process of extracting the meaning of an utterance an expression in some representation language. 

Description : Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex ... solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Last Answer : d) Because it is the simplest linearly inseparable problem that exists.

Description : This set of Artificial Intelligence MCQs focuses on Neural Networks - 2 . 1. Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in ... by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

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Description : Traditional set theory is also known as Crisp Set theory. a) True b) False

Last Answer : a) True

Description : Traditional set theory is also known as Crisp Set theory. a) True b) False

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Description : Basic idea of an partitioned nets is to break network into spaces which consist of groups of nodes and arcs and regard each space as a node. a) True b) False

Last Answer : a) True

Description : Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

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Description : Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in ... ) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

Last Answer : a) (ii) and (iii) are true

Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

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Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned

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Description : Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

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Description : Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a ... ) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

Last Answer : a) (ii) and (iii) are true

Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned

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Description : A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0. a) True b) False c) Sometimes – it can also output intermediate values as well d) Can’t say

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