spaCy/website/docs/api/kb.md
Sofie Van Landeghem 0b4b4f1819 Documentation for Entity Linking (#4065)
* document token ent_kb_id

* document span kb_id

* update pipeline documentation

* prior and context weights as bool's instead

* entitylinker api documentation

* drop for both models

* finish entitylinker documentation

* small fixes

* documentation for KB

* candidate documentation

* links to api pages in code

* small fix

* frequency examples as counts for consistency

* consistent documentation about tensors returned by predict

* add entity linking to usage 101

* add entity linking infobox and KB section to 101

* entity-linking in linguistic features

* small typo corrections

* training example and docs for entity_linker

* predefined nlp and kb

* revert back to similarity encodings for simplicity (for now)

* set prior probabilities to 0 when excluded

* code clean up

* bugfix: deleting kb ID from tokens when entities were removed

* refactor train el example to use either model or vocab

* pretrain_kb example for example kb generation

* add to training docs for KB + EL example scripts

* small fixes

* error numbering

* ensure the language of vocab and nlp stay consistent across serialization

* equality with =

* avoid conflict in errors file

* add error 151

* final adjustements to the train scripts - consistency

* update of goldparse documentation

* small corrections

* push commit

* typo fix

* add candidate API to kb documentation

* update API sidebar with EntityLinker and KnowledgeBase

* remove EL from 101 docs

* remove entity linker from 101 pipelines / rephrase

* custom el model instead of existing model

* set version to 2.2 for EL functionality

* update documentation for 2 CLI scripts
2019-09-12 11:38:34 +02:00

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Markdown

---
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 pre-trained 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` | unicode | The unique entity identifier |
| `freq` | float | The frequency of the entity in a typical corpus |
| `entity_vector` | vector | The pre-trained 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` | unicode | 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` | unicode | 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 pre-trained entity vector.
> #### Example
>
> ```python
> vector = kb.get_vector("Q42")
> ```
| Name | Type | Description |
| ------------- | ------------- | -------------------------------------------------- |
| `entity` | unicode | 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` | unicode | The entity ID |
| `alias` | unicode | 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` | unicode / `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` | unicode / `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_` | unicode | The entity's unique KB identifier |
| `alias` | int | The alias or textual mention |
| `alias_` | unicode | 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 pre-trained vector of the entity |