📝 Chapter 3 Quiz
Advanced Knowledge Graphs
Test Your Understanding
Passing Score:
7/10 (70%)
Question 1 of 10
Graph embeddings preserve which property of the network?
A) Visual appearance only
B) Network structure and relationships
C) File size
D) User preferences
✓ Correct!
Embeddings preserve structural properties.
Question 2 of 10
What is Node2Vec based on?
A) Random walks through the graph
B) Neural networks only
C) Genetic algorithms
D) User input
✓ Correct!
Node2Vec uses biased random walks.
Question 3 of 10
Which is a graph embedding technique?
A) TransE
B) CSS styling
C) HTML markup
D) REST API
✓ Correct!
TransE is a translation-based embedding method.
Question 4 of 10
Graph embeddings help with finding
A) Visualization only
B) Similar entities in the network
C) Data storage optimization
D) Nothing useful
✓ Correct!
Similarity is measured in embedding space.
Question 5 of 10
Embeddings are typically how many dimensions?
A) 1-2 dimensions
B) 5-10 dimensions
C) 100-300 dimensions
D) Infinite dimensions
✓ Correct!
High dimensions capture rich relationships.
Question 6 of 10
TransE models relationships as
A) Translations in embedding space
B) Rotations only
C) Reflections
D) Scaling vectors
✓ Correct!
TransE: head + relation ≈ tail.
Question 7 of 10
Graph embeddings work best with
A) Very small graphs
B) Large connected networks
C) Isolated nodes only
D) Text documents
✓ Correct!
Embeddings capture network structure.
Question 8 of 10
Similarity in embedding space uses
A) Euclidean distance or cosine similarity
B) Random distance
C) No distance metric
D) Time-based similarity
✓ Correct!
Standard distance metrics work in embedding space.
Question 9 of 10
Knowledge graph completion means
A) Finishing documentation
B) Predicting missing relationships
C) Deleting unnecessary nodes
D) Adding user comments
✓ Correct!
Embeddings enable link prediction.
Question 10 of 10
Embeddings primarily enable
A) Only visualization
B) Machine learning on graph data
C) User interface design
D) Database backup
✓ Correct!
Embeddings bridge graphs and ML.
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Passing score: 7/10 (70%)