mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
3b8918e166
* API docs: Rename kb_in_memory to inmemorylookupkb, add to sidebar * adjust to mdx * linkout to InMemoryLookupKB at first occurrence in kb.mdx * fix links to docs * revert Azure trigger setting (I'll make a separate PR) Co-authored-by: svlandeg <svlandeg@github.com>
233 lines
10 KiB
Plaintext
233 lines
10 KiB
Plaintext
---
|
|
title: KnowledgeBase
|
|
teaser:
|
|
A storage class for entities and aliases of a specific knowledge base
|
|
(ontology)
|
|
tag: class
|
|
source: spacy/kb/kb.pyx
|
|
version: 2.2
|
|
---
|
|
|
|
The `KnowledgeBase` object is an abstract class providing a method to generate
|
|
[`Candidate`](/api/kb#candidate) 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.
|
|
|
|
Beyond that, `KnowledgeBase` classes have to implement a number of utility
|
|
functions called by the [`EntityLinker`](/api/entitylinker) component.
|
|
|
|
<Infobox variant="warning">
|
|
|
|
This class was not abstract up to spaCy version 3.5. The `KnowledgeBase`
|
|
implementation up to that point is available as
|
|
[`InMemoryLookupKB`](/api/inmemorylookupkb) from 3.5 onwards.
|
|
|
|
</Infobox>
|
|
|
|
## KnowledgeBase.\_\_init\_\_ {id="init",tag="method"}
|
|
|
|
`KnowledgeBase` is an abstract class and cannot be instantiated. Its child
|
|
classes should call `__init__()` to set up some necessary attributes.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.kb import KnowledgeBase
|
|
> from spacy.vocab import Vocab
|
|
>
|
|
> class FullyImplementedKB(KnowledgeBase):
|
|
> def __init__(self, vocab: Vocab, entity_vector_length: int):
|
|
> super().__init__(vocab, entity_vector_length)
|
|
> ...
|
|
> vocab = nlp.vocab
|
|
> kb = FullyImplementedKB(vocab=vocab, entity_vector_length=64)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ---------------------- | ------------------------------------------------ |
|
|
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
|
| `entity_vector_length` | Length of the fixed-size entity vectors. ~~int~~ |
|
|
|
|
## KnowledgeBase.entity_vector_length {id="entity_vector_length",tag="property"}
|
|
|
|
The length of the fixed-size entity vectors in the knowledge base.
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------ |
|
|
| **RETURNS** | Length of the fixed-size entity vectors. ~~int~~ |
|
|
|
|
## KnowledgeBase.get_candidates {id="get_candidates",tag="method"}
|
|
|
|
Given a certain textual mention as input, retrieve a list of candidate entities
|
|
of type [`Candidate`](/api/kb#candidate).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.lang.en import English
|
|
> nlp = English()
|
|
> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
|
|
> candidates = kb.get_candidates(doc[0:2])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------------------------------------------- |
|
|
| `mention` | The textual mention or alias. ~~Span~~ |
|
|
| **RETURNS** | An iterable of relevant `Candidate` objects. ~~Iterable[Candidate]~~ |
|
|
|
|
## KnowledgeBase.get_candidates_batch {id="get_candidates_batch",tag="method"}
|
|
|
|
Same as [`get_candidates()`](/api/kb#get_candidates), but for an arbitrary
|
|
number of mentions. The [`EntityLinker`](/api/entitylinker) component will call
|
|
`get_candidates_batch()` instead of `get_candidates()`, if the config parameter
|
|
`candidates_batch_size` is greater or equal than 1.
|
|
|
|
The default implementation of `get_candidates_batch()` executes
|
|
`get_candidates()` in a loop. We recommend implementing a more efficient way to
|
|
retrieve candidates for multiple mentions at once, if performance is of concern
|
|
to you.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.lang.en import English
|
|
> nlp = English()
|
|
> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
|
|
> candidates = kb.get_candidates((doc[0:2], doc[3:]))
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------------------------------------------------------------------- |
|
|
| `mentions` | The textual mention or alias. ~~Iterable[Span]~~ |
|
|
| **RETURNS** | An iterable of iterable with relevant `Candidate` objects. ~~Iterable[Iterable[Candidate]]~~ |
|
|
|
|
## KnowledgeBase.get_alias_candidates {id="get_alias_candidates",tag="method"}
|
|
|
|
<Infobox variant="warning">
|
|
This method is _not_ available from spaCy 3.5 onwards.
|
|
</Infobox>
|
|
|
|
From spaCy 3.5 on `KnowledgeBase` is an abstract class (with
|
|
[`InMemoryLookupKB`](/api/inmemorylookupkb) being a drop-in replacement) to
|
|
allow more flexibility in customizing knowledge bases. Some of its methods were
|
|
moved to [`InMemoryLookupKB`](/api/inmemorylookupkb) during this refactoring,
|
|
one of those being `get_alias_candidates()`. This method is now available as
|
|
[`InMemoryLookupKB.get_alias_candidates()`](/api/inmemorylookupkb#get_alias_candidates).
|
|
Note:
|
|
[`InMemoryLookupKB.get_candidates()`](/api/inmemorylookupkb#get_candidates)
|
|
defaults to
|
|
[`InMemoryLookupKB.get_alias_candidates()`](/api/inmemorylookupkb#get_alias_candidates).
|
|
|
|
## KnowledgeBase.get_vector {id="get_vector",tag="method"}
|
|
|
|
Given a certain entity ID, retrieve its pretrained entity vector.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> vector = kb.get_vector("Q42")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------------- |
|
|
| `entity` | The entity ID. ~~str~~ |
|
|
| **RETURNS** | The entity vector. ~~Iterable[float]~~ |
|
|
|
|
## KnowledgeBase.get_vectors {id="get_vectors",tag="method"}
|
|
|
|
Same as [`get_vector()`](/api/kb#get_vector), but for an arbitrary number of
|
|
entity IDs.
|
|
|
|
The default implementation of `get_vectors()` executes `get_vector()` in a loop.
|
|
We recommend implementing a more efficient way to retrieve vectors for multiple
|
|
entities at once, if performance is of concern to you.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> vectors = kb.get_vectors(("Q42", "Q3107329"))
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------------------------------- |
|
|
| `entities` | The entity IDs. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The entity vectors. ~~Iterable[Iterable[numpy.ndarray]]~~ |
|
|
|
|
## KnowledgeBase.to_disk {id="to_disk",tag="method"}
|
|
|
|
Save the current state of the knowledge base to a directory.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> kb.to_disk(path)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `path` | 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]~~ |
|
|
| `exclude` | List of components to exclude. ~~Iterable[str]~~ |
|
|
|
|
## KnowledgeBase.from_disk {id="from_disk",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.vocab import Vocab
|
|
> vocab = Vocab().from_disk("/path/to/vocab")
|
|
> kb = FullyImplementedKB(vocab=vocab, entity_vector_length=64)
|
|
> kb.from_disk("/path/to/kb")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ----------------------------------------------------------------------------------------------- |
|
|
| `loc` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
|
|
| `exclude` | List of components to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The modified `KnowledgeBase` object. ~~KnowledgeBase~~ |
|
|
|
|
## Candidate {id="candidate",tag="class"}
|
|
|
|
A `Candidate` object refers to a textual mention (alias) that may or may not be
|
|
resolved to a specific entity from a `KnowledgeBase`. This will be used as input
|
|
for the entity linking algorithm which will disambiguate the various candidates
|
|
to the correct one. Each candidate `(alias, entity)` pair is assigned to a
|
|
certain prior probability.
|
|
|
|
### Candidate.\_\_init\_\_ {id="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` method of the
|
|
[`entity_linker`](/api/entitylinker) pipe.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.kb import Candidate
|
|
> candidate = Candidate(kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ------------- | ------------------------------------------------------------------------- |
|
|
| `kb` | The knowledge base that defined this candidate. ~~KnowledgeBase~~ |
|
|
| `entity_hash` | The hash of the entity's KB ID. ~~int~~ |
|
|
| `entity_freq` | The entity frequency as recorded in the KB. ~~float~~ |
|
|
| `alias_hash` | The hash of the textual mention or alias. ~~int~~ |
|
|
| `prior_prob` | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
|
|
|
|
## Candidate attributes {id="candidate-attributes"}
|
|
|
|
| Name | Description |
|
|
| --------------- | ------------------------------------------------------------------------ |
|
|
| `entity` | The entity's unique KB identifier. ~~int~~ |
|
|
| `entity_` | The entity's unique KB identifier. ~~str~~ |
|
|
| `alias` | The alias or textual mention. ~~int~~ |
|
|
| `alias_` | The alias or textual mention. ~~str~~ |
|
|
| `prior_prob` | The prior probability of the `alias` referring to the `entity`. ~~long~~ |
|
|
| `entity_freq` | The frequency of the entity in a typical corpus. ~~long~~ |
|
|
| `entity_vector` | The pretrained vector of the entity. ~~numpy.ndarray~~ |
|