# 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:

#### 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

Now we add another fact:

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.

## Collecting knowledge