Description

A Semantic Network is a knowledge representation technique used in Artificial Intelligence that shows relationships between concepts in the form of a graph. Each node represents an entity or concept, and each edge represents a relationship between them. These networks help systems reason about entities and their interconnections — especially useful in natural language processing, ontologies, and expert systems.

Examples (Code)

Below is a simple example

#Example: Representing a Semantic Network using NetworkX
import networkx as nx
import matplotlib.pyplot as plt

# Create a directed graph
G = nx.DiGraph()

# Add nodes (concepts)
G.add_node("Dog")
G.add_node("Animal")
G.add_node("Mammal")
G.add_node("Barks")

# Add relationships (edges)
G.add_edge("Dog", "Animal", label="is-a")
G.add_edge("Dog", "Mammal", label="is-a")
G.add_edge("Dog", "Barks", label="can")

# Draw the graph with labels
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=3000, font_size=10, font_weight='bold')
edge_labels = nx.get_edge_attributes(G, 'label')
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
plt.title("Semantic Network Example")
plt.show()

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