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277 lines
11 KiB
Markdown
277 lines
11 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) 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 | Description |
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| ---------------------- | ------------------------------------------------ |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `entity_vector_length` | Length of the fixed-size entity vectors. ~~int~~ |
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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 | Description |
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| ----------- | ------------------------------------------------ |
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| **RETURNS** | Length of the fixed-size entity vectors. ~~int~~ |
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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 | Description |
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| --------------- | ---------------------------------------------------------- |
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| `entity` | The unique entity identifier. ~~str~~ |
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| `freq` | The frequency of the entity in a typical corpus. ~~float~~ |
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| `entity_vector` | The pretrained vector of the entity. ~~numpy.ndarray~~ |
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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 | Description |
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| ------------- | ---------------------------------------------------------------- |
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| `entity_list` | List of unique entity identifiers. ~~Iterable[Union[str, int]]~~ |
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| `freq_list` | List of entity frequencies. ~~Iterable[int]~~ |
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| `vector_list` | List of entity vectors. ~~Iterable[numpy.ndarray]~~ |
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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. Note that an empty string can not be used as alias.
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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 | Description |
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| --------------- | --------------------------------------------------------------------------------- |
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| `alias` | The textual mention or alias. Can not be the empty string. ~~str~~ |
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| `entities` | The potential entities that the alias may refer to. ~~Iterable[Union[str, int]]~~ |
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| `probabilities` | The prior probabilities of each entity. ~~Iterable[float]~~ |
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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 | Description |
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| ----------- | ----------------------------------------------------- |
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| **RETURNS** | The number of entities in the knowledge base. ~~int~~ |
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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 | Description |
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| ----------- | --------------------------------------------------------- |
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| **RETURNS** | The list of entities in the knowledge base. ~~List[str]~~ |
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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 | Description |
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| ----------- | ---------------------------------------------------- |
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| **RETURNS** | The number of aliases in the knowledge base. ~~int~~ |
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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 | Description |
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| ----------- | -------------------------------------------------------- |
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| **RETURNS** | The list of aliases in the knowledge base. ~~List[str]~~ |
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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).
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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 | Description |
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| ----------- | ------------------------------------- |
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| `alias` | The textual mention or alias. ~~str~~ |
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| **RETURNS** | iterable | The list of relevant `Candidate` objects. ~~List[Candidate]~~ |
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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 | Description |
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| ----------- | ------------------------------------ |
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| `entity` | The entity ID. ~~str~~ |
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| **RETURNS** | The entity vector. ~~numpy.ndarray~~ |
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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 | Description |
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| ----------- | ------------------------------------------------------------------------- |
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| `entity` | The entity ID. ~~str~~ |
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| `alias` | The textual mention or alias. ~~str~~ |
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| **RETURNS** | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
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## KnowledgeBase.to_disk {#to_disk 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.to_disk(loc)
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> ```
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| Name | Description |
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| ----- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `loc` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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## KnowledgeBase.from_disk {#from_disk 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.from_disk("/path/to/kb")
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> ```
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| Name | Description |
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| ----------- | ----------------------------------------------------------------------------------------------- |
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| `loc` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| **RETURNS** | The modified `KnowledgeBase` object. ~~KnowledgeBase~~ |
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## Candidate {#candidate tag="class"}
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A `Candidate` object refers to a textual mention (alias) that may or may not be
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resolved to a specific entity from a `KnowledgeBase`. This will be used as input
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for the entity linking algorithm which will disambiguate the various candidates
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to the correct one. Each candidate `(alias, entity)` pair is assigned to a
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certain prior probability.
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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 | Description |
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| ------------- | ------------------------------------------------------------------------- |
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| `kb` | The knowledge base that defined this candidate. ~~KnowledgeBase~~ |
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| `entity_hash` | The hash of the entity's KB ID. ~~int~~ |
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| `entity_freq` | The entity frequency as recorded in the KB. ~~float~~ |
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| `alias_hash` | The hash of the textual mention or alias. ~~int~~ |
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| `prior_prob` | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
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## Candidate attributes {#candidate-attributes}
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| Name | Description |
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| --------------- | ------------------------------------------------------------------------ |
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| `entity` | The entity's unique KB identifier. ~~int~~ |
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| `entity_` | The entity's unique KB identifier. ~~str~~ |
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| `alias` | The alias or textual mention. ~~int~~ |
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| `alias_` | The alias or textual mention. ~~str~~ |
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| `prior_prob` | The prior probability of the `alias` referring to the `entity`. ~~long~~ |
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| `entity_freq` | The frequency of the entity in a typical corpus. ~~long~~ |
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| `entity_vector` | The pretrained vector of the entity. ~~numpy.ndarray~~ |
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