KnowledgeBase · spaCy API Documentation

The KnowledgeBase object provides a method to generate 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.

KnowledgeBase.init method

Create the knowledge base.

Name	Description
`vocab`	The shared vocabulary. `Vocab`
`entity_vector_length`	Length of the fixed-size entity vectors. `int`

KnowledgeBase.entity_vector_length 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.add_entity method

Add an entity to the knowledge base, specifying its corpus frequency and entity vector, which should be of length entity_vector_length.

Name	Description
`entity`	The unique entity identifier. `str`
`freq`	The frequency of the entity in a typical corpus. `float`
`entity_vector`	The pretrained vector of the entity. `numpy.ndarray`

KnowledgeBase.set_entities method

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

Name	Description
`entity_list`	List of unique entity identifiers. `Iterable[Union[str, int]]`
`freq_list`	List of entity frequencies. `Iterable[int]`
`vector_list`	List of entity vectors. `Iterable[numpy.ndarray]`

KnowledgeBase.add_alias 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 or set_entities. The sum of the prior probabilities should not exceed 1. Note that an empty string can not be used as alias.

Name	Description
`alias`	The textual mention or alias. Can not be the empty string. `str`
`entities`	The potential entities that the alias may refer to. `Iterable[Union[str, int]]`
`probabilities`	The prior probabilities of each entity. `Iterable[float]`

KnowledgeBase.len method

Get the total number of entities in the knowledge base.

Name	Description
RETURNS	The number of entities in the knowledge base. `int`

KnowledgeBase.get_entity_strings method

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

Name	Description
RETURNS	The list of entities in the knowledge base. `List[str]`

KnowledgeBase.get_size_aliases method

Get the total number of aliases in the knowledge base.

Name	Description
RETURNS	The number of aliases in the knowledge base. `int`

KnowledgeBase.get_alias_strings method

Get a list of all aliases in the knowledge base.

Name	Description
RETURNS	The list of aliases in the knowledge base. `List[str]`

KnowledgeBase.get_alias_candidates method

Given a certain textual mention as input, retrieve a list of candidate entities of type Candidate.

Name	Description
`alias`	The textual mention or alias. `str`
RETURNS	The list of relevant `Candidate` objects. `List[Candidate]`

KnowledgeBase.get_vector method

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

Name	Description
`entity`	The entity ID. `str`
RETURNS	The entity vector. `numpy.ndarray`

KnowledgeBase.get_prior_prob 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.

Name	Description
`entity`	The entity ID. `str`
`alias`	The textual mention or alias. `str`
RETURNS	The prior probability of the `alias` referring to the `entity`. `float`

KnowledgeBase.to_disk method

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

Name	Description
`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]`

KnowledgeBase.from_disk method

Restore the state of the knowledge base from a given directory. Note that the Vocab should also be the same as the one used to create the KB.

Example

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.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]`
RETURNS	The modified `KnowledgeBase` object. `KnowledgeBase`

Candidate 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 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 pipe.

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

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`