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269 lines
10 KiB
Markdown
269 lines
10 KiB
Markdown
---
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title: KnowledgeBase
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teaser:
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A storage class for entities and aliases of a specific knowledge base
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(ontology)
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tag: class
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source: spacy/kb.pyx
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new: 2.2
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---
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The `KnowledgeBase` object provides a method to generate
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[`Candidate`](/api/kb/#candidate_init) objects, which are plausible external
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identifiers given a certain textual mention. Each such `Candidate` holds
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information from the relevant KB entities, such as its frequency in text and
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possible aliases. Each entity in the knowledge base also has a pretrained entity
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vector of a fixed size.
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## KnowledgeBase.\_\_init\_\_ {#init tag="method"}
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Create the knowledge base.
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> #### Example
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>
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> ```python
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> from spacy.kb import KnowledgeBase
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> vocab = nlp.vocab
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> kb = KnowledgeBase(vocab=vocab, entity_vector_length=64)
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> ```
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| Name | Type | Description |
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| ---------------------- | ------- | ---------------------------------------- |
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| `vocab` | `Vocab` | A `Vocab` object. |
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| `entity_vector_length` | int | Length of the fixed-size entity vectors. |
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## KnowledgeBase.entity_vector_length {#entity_vector_length tag="property"}
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The length of the fixed-size entity vectors in the knowledge base.
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| Name | Type | Description |
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| ----------- | ---- | ---------------------------------------- |
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| **RETURNS** | int | Length of the fixed-size entity vectors. |
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## KnowledgeBase.add_entity {#add_entity tag="method"}
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Add an entity to the knowledge base, specifying its corpus frequency and entity
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vector, which should be of length
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[`entity_vector_length`](/api/kb#entity_vector_length).
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> #### Example
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>
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> ```python
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> kb.add_entity(entity="Q42", freq=32, entity_vector=vector1)
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> kb.add_entity(entity="Q463035", freq=111, entity_vector=vector2)
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> ```
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| Name | Type | Description |
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| --------------- | ------ | ----------------------------------------------- |
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| `entity` | str | The unique entity identifier |
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| `freq` | float | The frequency of the entity in a typical corpus |
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| `entity_vector` | vector | The pretrained vector of the entity |
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## KnowledgeBase.set_entities {#set_entities tag="method"}
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Define the full list of entities in the knowledge base, specifying the corpus
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frequency and entity vector for each entity.
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> #### Example
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>
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> ```python
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> kb.set_entities(entity_list=["Q42", "Q463035"], freq_list=[32, 111], vector_list=[vector1, vector2])
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> ```
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| Name | Type | Description |
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| ------------- | -------- | --------------------------------- |
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| `entity_list` | iterable | List of unique entity identifiers |
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| `freq_list` | iterable | List of entity frequencies |
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| `vector_list` | iterable | List of entity vectors |
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## KnowledgeBase.add_alias {#add_alias tag="method"}
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Add an alias or mention to the knowledge base, specifying its potential KB
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identifiers and their prior probabilities. The entity identifiers should refer
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to entities previously added with [`add_entity`](/api/kb#add_entity) or
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[`set_entities`](/api/kb#set_entities). The sum of the prior probabilities
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should not exceed 1.
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> #### Example
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>
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> ```python
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> kb.add_alias(alias="Douglas", entities=["Q42", "Q463035"], probabilities=[0.6, 0.3])
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> ```
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| Name | Type | Description |
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| --------------- | -------- | -------------------------------------------------- |
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| `alias` | str | The textual mention or alias |
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| `entities` | iterable | The potential entities that the alias may refer to |
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| `probabilities` | iterable | The prior probabilities of each entity |
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## KnowledgeBase.\_\_len\_\_ {#len tag="method"}
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Get the total number of entities in the knowledge base.
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> #### Example
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>
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> ```python
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> total_entities = len(kb)
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> ```
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| Name | Type | Description |
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| ----------- | ---- | --------------------------------------------- |
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| **RETURNS** | int | The number of entities in the knowledge base. |
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## KnowledgeBase.get_entity_strings {#get_entity_strings tag="method"}
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Get a list of all entity IDs in the knowledge base.
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> #### Example
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>
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> ```python
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> all_entities = kb.get_entity_strings()
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> ```
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| Name | Type | Description |
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| ----------- | ---- | ------------------------------------------- |
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| **RETURNS** | list | The list of entities in the knowledge base. |
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## KnowledgeBase.get_size_aliases {#get_size_aliases tag="method"}
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Get the total number of aliases in the knowledge base.
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> #### Example
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>
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> ```python
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> total_aliases = kb.get_size_aliases()
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> ```
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| Name | Type | Description |
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| ----------- | ---- | -------------------------------------------- |
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| **RETURNS** | int | The number of aliases in the knowledge base. |
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## KnowledgeBase.get_alias_strings {#get_alias_strings tag="method"}
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Get a list of all aliases in the knowledge base.
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> #### Example
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>
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> ```python
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> all_aliases = kb.get_alias_strings()
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> ```
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| Name | Type | Description |
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| ----------- | ---- | ------------------------------------------ |
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| **RETURNS** | list | The list of aliases in the knowledge base. |
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## KnowledgeBase.get_candidates {#get_candidates tag="method"}
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Given a certain textual mention as input, retrieve a list of candidate entities
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of type [`Candidate`](/api/kb/#candidate_init).
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> #### Example
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>
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> ```python
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> candidates = kb.get_candidates("Douglas")
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> ```
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| Name | Type | Description |
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| ----------- | -------- | ---------------------------------------- |
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| `alias` | str | The textual mention or alias |
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| **RETURNS** | iterable | The list of relevant `Candidate` objects |
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## KnowledgeBase.get_vector {#get_vector tag="method"}
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Given a certain entity ID, retrieve its pretrained entity vector.
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> #### Example
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>
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> ```python
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> vector = kb.get_vector("Q42")
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> ```
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| Name | Type | Description |
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| ----------- | ------ | ----------------- |
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| `entity` | str | The entity ID |
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| **RETURNS** | vector | The entity vector |
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## KnowledgeBase.get_prior_prob {#get_prior_prob tag="method"}
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Given a certain entity ID and a certain textual mention, retrieve the prior
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probability of the fact that the mention links to the entity ID.
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> #### Example
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>
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> ```python
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> probability = kb.get_prior_prob("Q42", "Douglas")
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> ```
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| Name | Type | Description |
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| ----------- | ----- | -------------------------------------------------------------- |
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| `entity` | str | The entity ID |
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| `alias` | str | The textual mention or alias |
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| **RETURNS** | float | The prior probability of the `alias` referring to the `entity` |
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## KnowledgeBase.dump {#dump tag="method"}
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Save the current state of the knowledge base to a directory.
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> #### Example
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>
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> ```python
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> kb.dump(loc)
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> ```
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| Name | Type | Description |
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| ----- | ------------ | --------------------------------------------------------------------------------------------------------------------- |
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| `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. |
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## KnowledgeBase.load_bulk {#load_bulk tag="method"}
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Restore the state of the knowledge base from a given directory. Note that the
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[`Vocab`](/api/vocab) should also be the same as the one used to create the KB.
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> #### Example
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>
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> ```python
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> from spacy.kb import KnowledgeBase
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> from spacy.vocab import Vocab
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> vocab = Vocab().from_disk("/path/to/vocab")
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> kb = KnowledgeBase(vocab=vocab, entity_vector_length=64)
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> kb.load_bulk("/path/to/kb")
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> ```
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| Name | Type | Description |
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| ----------- | --------------- | -------------------------------------------------------------------------- |
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| `loc` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
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| **RETURNS** | `KnowledgeBase` | The modified `KnowledgeBase` object. |
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## Candidate.\_\_init\_\_ {#candidate_init tag="method"}
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Construct a `Candidate` object. Usually this constructor is not called directly,
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but instead these objects are returned by the
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[`get_candidates`](/api/kb#get_candidates) method of a `KnowledgeBase`.
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> #### Example
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>
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> ```python
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> from spacy.kb import Candidate
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> candidate = Candidate(kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob)
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> ```
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| Name | Type | Description |
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| ------------- | --------------- | -------------------------------------------------------------- |
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| `kb` | `KnowledgeBase` | The knowledge base that defined this candidate. |
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| `entity_hash` | int | The hash of the entity's KB ID. |
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| `entity_freq` | float | The entity frequency as recorded in the KB. |
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| `alias_hash` | int | The hash of the textual mention or alias. |
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| `prior_prob` | float | The prior probability of the `alias` referring to the `entity` |
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## Candidate attributes {#candidate_attributes}
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| Name | Type | Description |
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| --------------- | ------ | -------------------------------------------------------------- |
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| `entity` | int | The entity's unique KB identifier |
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| `entity_` | str | The entity's unique KB identifier |
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| `alias` | int | The alias or textual mention |
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| `alias_` | str | The alias or textual mention |
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| `prior_prob` | long | The prior probability of the `alias` referring to the `entity` |
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| `entity_freq` | long | The frequency of the entity in a typical corpus |
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| `entity_vector` | vector | The pretrained vector of the entity |
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