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	Type	Description
vocab	Vocab	A Vocab object.
entity_vector_length	int	Length of the fixed-size entity vectors.

KnowledgeBase.entity_vector_length

The length of the fixed-size entity vectors in the knowledge base.

Name	Type	Description
RETURNS	int	Length of the fixed-size entity vectors.

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	Type	Description
entity	str	The unique entity identifier
freq	float	The frequency of the entity in a typical corpus
entity_vector	vector	The pretrained vector of the entity

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	Type	Description
entity_list	iterable	List of unique entity identifiers
freq_list	iterable	List of entity frequencies
vector_list	iterable	List of entity vectors

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	Type	Description
alias	str	The textual mention or alias
entities	iterable	The potential entities that the alias may refer to
probabilities	iterable	The prior probabilities of each entity

KnowledgeBase.__len__

Get the total number of entities in the knowledge base.

Example

total_entities = len(kb)

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

KnowledgeBase.get_entity_strings

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

Example

all_entities = kb.get_entity_strings()

Name	Type	Description
RETURNS	list	The list of entities in the knowledge base.

KnowledgeBase.get_size_aliases

Get the total number of aliases in the knowledge base.

Example

total_aliases = kb.get_size_aliases()

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

KnowledgeBase.get_alias_strings

Get a list of all aliases in the knowledge base.

Example

all_aliases = kb.get_alias_strings()

Name	Type	Description
RETURNS	list	The list of aliases in the knowledge base.

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	Type	Description
alias	str	The textual mention or alias
RETURNS	iterable	The list of relevant Candidate objects

KnowledgeBase.get_vector

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

Example

vector = kb.get_vector("Q42")

Name	Type	Description
entity	str	The entity ID
RETURNS	vector	The entity vector

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

KnowledgeBase.dump

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

Example

kb.dump(loc)

Name	Type	Description
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.

KnowledgeBase.load_bulk

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.load_bulk("/path/to/kb")

Name	Type	Description
loc	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
RETURNS	KnowledgeBase	The modified KnowledgeBase object.

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	Type	Description
kb	KnowledgeBase	The knowledge base that defined this candidate.
entity_hash	int	The hash of the entity's KB ID.
entity_freq	float	The entity frequency as recorded in the KB.
alias_hash	int	The hash of the textual mention or alias.
prior_prob	float	The prior probability of the alias referring to the entity

Candidate attributes

Name	Type	Description
entity	int	The entity's unique KB identifier
entity_	str	The entity's unique KB identifier
alias	int	The alias or textual mention
alias_	str	The alias or textual mention
prior_prob	long	The prior probability of the alias referring to the entity
entity_freq	long	The frequency of the entity in a typical corpus
entity_vector	vector	The pretrained vector of the entity