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Concept
Entity linking
Summary
In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris". Entity linking is different from named-entity recognition (NER) in that NER identifies the occurrence of a named entity in text but it does not identify which specific entity it is (see Differences from other techniques).
In entity linking, words of interest (names of persons, locations and companies) are mapped from an input text to corresponding unique entities in a target knowledge base. Words of interest are called named entities (NEs), mentions, or surface forms. The target knowledge base depends on the intended application, but for entity linking systems intended to work on open-domain text it is common to use knowledge-bases derived from Wikipedia (such as Wikidata or DBpedia). In this case, each individual Wikipedia page is regarded as a separate entity. Entity linking techniques that map named entities to Wikipedia entities are also called wikification.
Considering again the example sentence "Paris is the capital of France", the expected output of an entity linking system will be Paris and France. These uniform resource locators (URLs) can be used as unique uniform resource identifiers
(URIs) for the entities in the knowledge base. Using a different knowledge base will return different URIs, but for knowledge bases built starting from Wikipedia there exist one-to-one URI mappings.
In most cases, knowledge bases are manually built, but in applications where large text corpora are available, the knowledge base can be inferred automatically from the available text.
Official source
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