spaCy/website/docs/api/kb.md

title	teaser	tag	source	new
KnowledgeBase	A storage class for entities and aliases of a specific knowledge base (ontology)	class	spacy/kb.pyx	2.2

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__

Create the knowledge base.

Example

from spacy.kb import KnowledgeBase
vocab = nlp.vocab
kb = KnowledgeBase(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

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

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

Example

kb.add_entity(entity="Q42", freq=32, entity_vector=vector1)
kb.add_entity(entity="Q463035", freq=111, entity_vector=vector2)

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

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

Example

kb.set_entities(entity_list=["Q42", "Q463035"], freq_list=[32, 111], vector_list=[vector1, vector2])

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

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.

Example

kb.add_alias(alias="Douglas", entities=["Q42", "Q463035"], probabilities=[0.6, 0.3])

Name	Description
alias	The textual mention or alias. 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__

Get the total number of entities in the knowledge base.

Example

total_entities = len(kb)

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

KnowledgeBase.get_entity_strings

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

Example

all_entities = kb.get_entity_strings()

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

KnowledgeBase.get_size_aliases

Get the total number of aliases in the knowledge base.

Example

total_aliases = kb.get_size_aliases()

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

KnowledgeBase.get_alias_strings

Get a list of all aliases in the knowledge base.

Example

all_aliases = kb.get_alias_strings()

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

KnowledgeBase.get_candidates

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

Example

candidates = kb.get_candidates("Douglas")

Name	Description
alias	The textual mention or alias. str
RETURNS	iterable

KnowledgeBase.get_vector

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

Example

vector = kb.get_vector("Q42")

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

KnowledgeBase.get_prior_prob

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

probability = kb.get_prior_prob("Q42", "Douglas")

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

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

Example

kb.to_disk(loc)

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

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

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__

Construct a Candidate object. Usually this constructor is not called directly, but instead these objects are returned by the get_candidates method of a KnowledgeBase.

Example

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

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