Entity$-$Based Retrieval Model $(12)$

Given below is a sample of TeKnowbase, a knowledge base of computer science concepts. The nodes are entities in Computer Science and the edges define relationships between them. Query: acoustic unit discovery using autoencoder You are given the following documents:

D$1$: reverberant speech recognition based on denoising autoencoder.

D$2$: hidden markov model variational autoencoder for acoustic unit discovery.

D$3$: pushing the limits of semi supervised learning for automatic speech recognition

$1)$ How many triples are present in the given knowledge graph? List all of them. $(1+2)$

$2)$ Represent each document using the bag$-$of$-$entities using the given knowledge graph $(3)$

$3)$ Represent the query using bag$-$of$-$entities model $(1)$

$4)$ Compute the similarity and the ranking for all $3$ documents using entity frequency $(3)$

$5)$ Consider the case of a new node and an edge being added to the sample graph as

shown below. Comment on how it will affect the ranking of documents for the query. $(2)$