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

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