spaCy/website/docs/api/kb.md

---
title: KnowledgeBase
teaser:
  A storage class for entities and aliases of a specific knowledge base
  (ontology)
tag: class
source: spacy/kb.pyx
new: 2.2
---

The `KnowledgeBase` object provides a method to generate
[`Candidate`](/api/kb/#candidate_init) objects, which are plausible external
identifiers given a certain textual mention. Each such `Candidate` holds
information from the relevant KB entities, such as its frequency in text and
possible aliases. Each entity in the knowledge base also has a pretrained entity
vector of a fixed size.

## KnowledgeBase.\_\_init\_\_ {#init tag="method"}

Create the knowledge base.

> #### Example
>
> ```python
> from spacy.kb import KnowledgeBase
> vocab = nlp.vocab
> kb = KnowledgeBase(vocab=vocab, entity_vector_length=64)
> ```

| Name                   | Type            | Description                              |
| ---------------------- | --------------- | ---------------------------------------- |
| `vocab`                | `Vocab`         | A `Vocab` object.                        |
| `entity_vector_length` | int             | Length of the fixed-size entity vectors. |
| **RETURNS**            | `KnowledgeBase` | The newly constructed object.            |

## KnowledgeBase.entity_vector_length {#entity_vector_length tag="property"}

The length of the fixed-size entity vectors in the knowledge base.

| Name        | Type | Description                              |
| ----------- | ---- | ---------------------------------------- |
| **RETURNS** | int  | Length of the fixed-size entity vectors. |

## KnowledgeBase.add_entity {#add_entity tag="method"}

Add an entity to the knowledge base, specifying its corpus frequency and entity
vector, which should be of length
[`entity_vector_length`](/api/kb#entity_vector_length).

> #### Example
>
> ```python
> kb.add_entity(entity="Q42", freq=32, entity_vector=vector1)
> kb.add_entity(entity="Q463035", freq=111, entity_vector=vector2)
> ```

| Name            | Type   | Description                                     |
| --------------- | ------ | ----------------------------------------------- |
| `entity`        | str    | The unique entity identifier                    |
| `freq`          | float  | The frequency of the entity in a typical corpus |
| `entity_vector` | vector | The pretrained vector of the entity             |

## KnowledgeBase.set_entities {#set_entities tag="method"}

Define the full list of entities in the knowledge base, specifying the corpus
frequency and entity vector for each entity.

> #### Example
>
> ```python
> kb.set_entities(entity_list=["Q42", "Q463035"], freq_list=[32, 111], vector_list=[vector1, vector2])
> ```

| Name          | Type     | Description                       |
| ------------- | -------- | --------------------------------- |
| `entity_list` | iterable | List of unique entity identifiers |
| `freq_list`   | iterable | List of entity frequencies        |
| `vector_list` | iterable | List of entity vectors            |

## KnowledgeBase.add_alias {#add_alias tag="method"}

Add an alias or mention to the knowledge base, specifying its potential KB
identifiers and their prior probabilities. The entity identifiers should refer
to entities previously added with [`add_entity`](/api/kb#add_entity) or
[`set_entities`](/api/kb#set_entities). The sum of the prior probabilities
should not exceed 1.

> #### Example
>
> ```python
> kb.add_alias(alias="Douglas", entities=["Q42", "Q463035"], probabilities=[0.6, 0.3])
> ```

| Name            | Type     | Description                                        |
| --------------- | -------- | -------------------------------------------------- |
| `alias`         | str      | The textual mention or alias                       |
| `entities`      | iterable | The potential entities that the alias may refer to |
| `probabilities` | iterable | The prior probabilities of each entity             |

## KnowledgeBase.\_\_len\_\_ {#len tag="method"}

Get the total number of entities in the knowledge base.

> #### Example
>
> ```python
> total_entities = len(kb)
> ```

| Name        | Type | Description                                   |
| ----------- | ---- | --------------------------------------------- |
| **RETURNS** | int  | The number of entities in the knowledge base. |

## KnowledgeBase.get_entity_strings {#get_entity_strings tag="method"}

Get a list of all entity IDs in the knowledge base.

> #### Example
>
> ```python
> all_entities = kb.get_entity_strings()
> ```

| Name        | Type | Description                                 |
| ----------- | ---- | ------------------------------------------- |
| **RETURNS** | list | The list of entities in the knowledge base. |

## KnowledgeBase.get_size_aliases {#get_size_aliases tag="method"}

Get the total number of aliases in the knowledge base.

> #### Example
>
> ```python
> total_aliases = kb.get_size_aliases()
> ```

| Name        | Type | Description                                  |
| ----------- | ---- | -------------------------------------------- |
| **RETURNS** | int  | The number of aliases in the knowledge base. |

## KnowledgeBase.get_alias_strings {#get_alias_strings tag="method"}

Get a list of all aliases in the knowledge base.

> #### Example
>
> ```python
> all_aliases = kb.get_alias_strings()
> ```

| Name        | Type | Description                                |
| ----------- | ---- | ------------------------------------------ |
| **RETURNS** | list | The list of aliases in the knowledge base. |

## KnowledgeBase.get_candidates {#get_candidates tag="method"}

Given a certain textual mention as input, retrieve a list of candidate entities
of type [`Candidate`](/api/kb/#candidate_init).

> #### Example
>
> ```python
> candidates = kb.get_candidates("Douglas")
> ```

| Name        | Type     | Description                              |
| ----------- | -------- | ---------------------------------------- |
| `alias`     | str      | The textual mention or alias             |
| **RETURNS** | iterable | The list of relevant `Candidate` objects |

## KnowledgeBase.get_vector {#get_vector tag="method"}

Given a certain entity ID, retrieve its pretrained entity vector.

> #### Example
>
> ```python
> vector = kb.get_vector("Q42")
> ```

| Name        | Type   | Description       |
| ----------- | ------ | ----------------- |
| `entity`    | str    | The entity ID     |
| **RETURNS** | vector | The entity vector |

## KnowledgeBase.get_prior_prob {#get_prior_prob tag="method"}

Given a certain entity ID and a certain textual mention, retrieve the prior
probability of the fact that the mention links to the entity ID.

> #### Example
>
> ```python
> probability = kb.get_prior_prob("Q42", "Douglas")
> ```

| Name        | Type  | Description                                                    |
| ----------- | ----- | -------------------------------------------------------------- |
| `entity`    | str   | The entity ID                                                  |
| `alias`     | str   | The textual mention or alias                                   |
| **RETURNS** | float | The prior probability of the `alias` referring to the `entity` |

## KnowledgeBase.dump {#dump tag="method"}

Save the current state of the knowledge base to a directory.

> #### Example
>
> ```python
> kb.dump(loc)
> ```

| Name  | Type         | Description                                                                                                           |
| ----- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
| `loc` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |

## KnowledgeBase.load_bulk {#load_bulk tag="method"}

Restore the state of the knowledge base from a given directory. Note that the
[`Vocab`](/api/vocab) should also be the same as the one used to create the KB.

> #### Example
>
> ```python
> from spacy.kb import KnowledgeBase
> from spacy.vocab import Vocab
> vocab = Vocab().from_disk("/path/to/vocab")
> kb = KnowledgeBase(vocab=vocab, entity_vector_length=64)
> kb.load_bulk("/path/to/kb")
> ```

| Name        | Type            | Description                                                                |
| ----------- | --------------- | -------------------------------------------------------------------------- |
| `loc`       | str / `Path`    | A path to a directory. Paths may be either strings or `Path`-like objects. |
| **RETURNS** | `KnowledgeBase` | The modified `KnowledgeBase` object.                                       |

## Candidate.\_\_init\_\_ {#candidate_init tag="method"}

Construct a `Candidate` object. Usually this constructor is not called directly,
but instead these objects are returned by the
[`get_candidates`](/api/kb#get_candidates) method of a `KnowledgeBase`.

> #### Example
>
> ```python
> from spacy.kb import Candidate
> candidate = Candidate(kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob)
> ```

| Name          | Type            | Description                                                    |
| ------------- | --------------- | -------------------------------------------------------------- |
| `kb`          | `KnowledgeBase` | The knowledge base that defined this candidate.                |
| `entity_hash` | int             | The hash of the entity's KB ID.                                |
| `entity_freq` | float           | The entity frequency as recorded in the KB.                    |
| `alias_hash`  | int             | The hash of the textual mention or alias.                      |
| `prior_prob`  | float           | The prior probability of the `alias` referring to the `entity` |
| **RETURNS**   | `Candidate`     | The newly constructed object.                                  |

## Candidate attributes {#candidate_attributes}

| Name            | Type   | Description                                                    |
| --------------- | ------ | -------------------------------------------------------------- |
| `entity`        | int    | The entity's unique KB identifier                              |
| `entity_`       | str    | The entity's unique KB identifier                              |
| `alias`         | int    | The alias or textual mention                                   |
| `alias_`        | str    | The alias or textual mention                                   |
| `prior_prob`    | long   | The prior probability of the `alias` referring to the `entity` |
| `entity_freq`   | long   | The frequency of the entity in a typical corpus                |
| `entity_vector` | vector | The pretrained vector of the entity                            |