Spacy Entity Linker is a pipeline for spaCy that performs Linked Entity Extraction with Wikidata on a given Document. The Entity Linking System operates by matching potential candidates from each sentence (subject, object, prepositional phrase, compounds, etc.) to aliases from Wikidata. The package allows to easily find the category behind each entity (e.g. "banana" is type "food" OR "Microsoft" is type "company"). It can is therefore useful for information extraction tasks and labeling tasks.
The package was written before a working Linked Entity Solution existed inside spaCy. In comparison to spaCy's linked entity system, it has the following advantages:
- no extensive training required (entity-matching via database)
- knowledge base can be dynamically updated without retraining
- entity categories can be easily resolved
- grouping entities by category
It also comes along with a number of disadvantages:
- it is slower than the spaCy implementation due to the use of a database for finding entities
- no context sensitivity due to the implementation of the "max-prior method" for entitiy disambiguation (an improved method for this is in progress)
To install the package, run:
pip install spacy-entity-linkerAfterwards, the knowledge base (Wikidata) must be downloaded. This can be either be done by manually calling
python -m spacy_entity_linker "download_knowledge_base"or when you first access the entity linker through spacy. This will download and extract a ~1.3GB file that contains a preprocessed version of Wikidata.
import spacy # version 3.5
# initialize language model
nlp = spacy.load("en_core_web_md")
# add pipeline (declared through entry_points in setup.py)
nlp.add_pipe("entityLinker", last=True)
doc = nlp("I watched the Pirates of the Caribbean last silvester")
# returns all entities in the whole document
all_linked_entities = doc._.linkedEntities
# iterates over sentences and prints linked entities
for sent in doc.sents:
sent._.linkedEntities.pretty_print()
# OUTPUT:
# https://www.wikidata.org/wiki/Q194318 Pirates of the Caribbean Series of fantasy adventure films
# https://www.wikidata.org/wiki/Q12525597 Silvester the day celebrated on 31 December (Roman Catholic Church) or 2 January (Eastern Orthodox Churches)
# entities are also directly accessible through spans
doc[3:7]._.linkedEntities.pretty_print()
# OUTPUT:
# https://www.wikidata.org/wiki/Q194318 Pirates of the Caribbean Series of fantasy adventure filmscontains an array of entity elements. It can be accessed like an array but also implements the following helper functions:
pretty_print()prints out information about all contained entitiesprint_super_classes()groups and prints all entites by their super class
doc = nlp("Elon Musk was born in South Africa. Bill Gates and Steve Jobs come from the United States")
doc._.linkedEntities.print_super_entities()
# OUTPUT:
# human (3) : Elon Musk,Bill Gates,Steve Jobs
# country (2) : South Africa,United States of America
# sovereign state (2) : South Africa,United States of America
# federal state (1) : United States of America
# constitutional republic (1) : United States of America
# democratic republic (1) : United States of Americaeach linked Entity is an object of type EntityElement. Each entity contains the methods
get_description()returns description from Wikidataget_id()returns Wikidata IDget_label()returns Wikidata labelget_span(doc)returns the span from the spacy document that contains the linked entity. You need to provide the currentdocas argument, in order to receive an actualspacy.tokens.Spanobject, otherwise you will receive aSpanInfoemulating the behaviour of a Spanget_url()returns the url to the corresponding Wikidata itempretty_print()prints out information about the entity elementget_sub_entities(limit=10)returns EntityCollection of all entities that derive from the current entityElement (e.g. fruit -> apple, banana, etc.)get_super_entities(limit=10)returns EntityCollection of all entities that the current entityElement derives from (e.g. New England Patriots -> Football Team))
Usage of the get_span method with SpanInfo:
import spacy
nlp = spacy.load('en_core_web_md')
nlp.add_pipe("entityLinker", last=True)
text = 'Apple is competing with Microsoft.'
doc = nlp(text)
sents = list(doc.sents)
ent = doc._.linkedEntities[0]
# using the SpanInfo class
span = ent.get_span()
print(span.start, span.end, span.text) # behaves like a Span
# check equivalence
print(span == doc[0:1]) # True
print(doc[0:1] == span) # TypeError: Argument 'other' has incorrect type (expected spacy.tokens.span.Span, got SpanInfo)
# now get the real span
span = ent.get_span(doc) # passing the doc instance here
print(span.start, span.end, span.text)
print(span == doc[0:1]) # True
print(doc[0:1] == span) # TrueIn the following example we will use SpacyEntityLinker to find find the mentioned Football Team in our text and explore other football teams of the same type
doc = nlp("I follow the New England Patriots")
patriots_entity = doc._.linkedEntities[0]
patriots_entity.pretty_print()
# OUTPUT:
# https://www.wikidata.org/wiki/Q193390
# New England Patriots
# National Football League franchise in Foxborough, Massachusetts
football_team_entity = patriots_entity.get_super_entities()[0]
football_team_entity.pretty_print()
# OUTPUT:
# https://www.wikidata.org/wiki/Q17156793
# American football team
# organization, in which a group of players are organized to compete as a team in American football
for child in football_team_entity.get_sub_entities(limit=32):
print(child)
# OUTPUT:
# New Orleans Saints
# New York Giants
# Pittsburgh Steelers
# New England Patriots
# Indianapolis Colts
# Miami Seahawks
# Dallas Cowboys
# Chicago Bears
# Washington Redskins
# Green Bay Packers
# ...Currently the only method for choosing an entity given different possible matches (e.g. Paris - city vs Paris - firstname) is max-prior. This method achieves around 70% accuracy on predicting the correct entities behind link descriptions on wikipedia.
The Entity Linker at the current state is still experimental and should not be used in production mode.
The current implementation supports only Sqlite. This is advantageous for development because it does not requirement any special setup and configuration. However, for more performance critical usecases, a different database with in-memory access (e.g. Redis) should be used. This may be implemented in the future.
the knowledge base was derived from this dataset: https://www.kaggle.com/kenshoresearch/kensho-derived-wikimedia-data
It was cleaned and post-procesed, including filtering out entities of "overrepresented" categories such as
- village in China
- train stations
- stars in the Galaxy
- etc.
The purpose behind the knowledge base cleaning was to reduce the knowledge base size, while keeping the most useful entities for general purpose applications.
Currently, the only way to change the knowledge base is a bit hacky and requires to replace or modify the underlying sqlite database. You will find it under site_packages/data_spacy_entity_linker/wikidb_filtered.db. The database contains 3 tables:
- aliases
- en_alias (english alias)
- en_alias_lowercase (english alias lowercased)
- joined
- en_label (label of the wikidata item)
- views (number of views of the corresponding wikipedia page (in a given period of time))
- inlinks (number of inlinks to the corresponding wikipedia page)
- item_id (wikidata id)
- description (description of the wikidata item)
- statements
- source_item_id (references item_id)
- target_item_id (references item_id)
- edge_property_id
- 279=subclass of (https://www.wikidata.org/wiki/Property:P279)
- 31=instance of (https://www.wikidata.org/wiki/Property:P31)
- 361=part of (https://www.wikidata.org/wiki/Property:P361)
spacy_entity_linker>=0.0(requiresspacy>=2.2,<3.0)spacy_entity_linker>=1.0(requiresspacy>=3.0)
- implement Entity Classifier based on sentence embeddings for improved accuracy
- implement get_picture_urls() on EntityElement
- retrieve statements for each EntityElement (inlinks + outlinks)
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