# Knowledge bases

## Storing knowledge

### Resource Description Framework (RDF)

#### Introduction

How can we store information like "Joe Blogs was born in the city London"?

Information is described as an RDF triple:

• Subject

• Predicate

• Object

#### Examples

"Joe Blogs was born in the city London" can be written as:

(Joe Blogs, BornCity, London)

#### Confidence

We can associate a confidence with each triple.

### Knowledge bases as graphs

#### Introduction

We can consider each fact to be a mini graph.

For "Joe Blogs was born in the city London" we have:

Joe Blogs $$\rightarrow^{was born in the city }$$ London

## Using knowledge

### Inferring facts

#### Introduction

London $$\rightarrow^{is a city in the country }$$ UK

We can use these to define a new predicate: "was born in the country" and generate the fact:

Joe Blogs $$\rightarrow^{was born in the country }$$ UK

### Relational learning

#### Introduction

Consider two facts:

Alice $$\rightarrow^{IsA }$$ Doctor

Bob $$\rightarrow^{HasMother }$$ Alice

We can consider another fact:

Bob $$\rightarrow^{IsA }$$ Doctor

#### Confidence of inferred facts

How confident should we be of this?

In practice the graph between Bob and Doctor will have many paths (qualifications)

### Prior and posterior confidence

#### Introduction

If we have a new fact, and a prior, we can create a posterior condfidence on the fact.