# 03 - Semantic Networks

Semantic networks are a knowledge representation scheme.

This lesson will cover the following topics:

• Knowlege representations
• Semantic networks
• Problem-solving with semantic networks
• Represent & Reason

## Representation

In each knowledge representation, there is a language, and that language has a vocabulary. In addition, the representation contains some content (or knowledge).

### Example: Newton's 2nd Law of Motion

$$F = ma$$ Force is equal to mass times acceleration

## Introduction to Semantic Networks

How to represent Raven’s Progressive Matrices using a semantic network. State A, and state B.

1. Label all objects (x is a circle, y is the diamond, z is the black dot), and reference them as nodes
2. Represent the relationships between nodes, in both states (frames), both A and B.
3. Represent the transformation between the nodes between states, A and B.

### Structure of Semantic Networks

• 1. Lexically: nodes
• 2. Structurally: directional links
• 3. Semantically: application-specific labels

### Characteristics of Good Representations

• Make relationships explicit
• exposese natural contraints
• bring objects and relations together
• exclude extraneous details
• transparent, concise, complete, fast, computable

## Guards and Prisoners Problem

### Description

• Three guard and three prisoners must cross river.
• Boat may take only one or two people at a time.
• Prisoners may never outnumber guards on either time (thought prisoner may be alone on either coast).

### Modeling using Semantic Nework

Lexicon: Consider each node to be a unique state, represented by: - number of prisoners and guards on left side - number of prisoners and guards on right side - side that boat is on.

Structure:

Semantic:

### Inference about State Transitions?

Which transitions (e.g. moves) between states are both legal AND productive? Represent total possible states given transformations possible: