Research any topic before you write.
Find related topics. | Discover entities. | See connections. | Build a topical map.
In natural language processing, entity linking, also referred to as named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD), named-entity normalization (NEN), or concept recognition, is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example…
Applications & Companies
Explore the main themes, entities and connections around Entity linking. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
entity linking entities knowledge wikipedia text base example paris disambiguation named france systems system also capital mathematical search textual features
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Entity linking | is a | critical step to bridge web data with knowledge bases | 0.90 | text |
| names | instance of | locates and classifies named entities in unstructured text into pre-defined categories | 0.80 | text |
| organizations | instance of | locates and classifies named entities in unstructured text into pre-defined categories | 0.80 | text |
| locations | instance of | locates and classifies named entities in unstructured text into pre-defined categories | 0.80 | text |
| and more | instance of | locates and classifies named entities in unstructured text into pre-defined categories | 0.80 | text |
| Wikipedia | instance of | Other approaches also collected training data based on unambiguous synonyms.Graph-basedModern entity linking systems also use large knowledge graphs created from knowledge bases | 0.80 | text |
| besides textual features generated from input documents or text corpora | instance of | Other approaches also collected training data based on unambiguous synonyms.Graph-basedModern entity linking systems also use large knowledge graphs created from knowledge bases | 0.80 | text |
| PageRank | instance of | algorithms | 0.80 | text |
| Wikipedia | instance of | Graph-basedModern entity linking systems also use large knowledge graphs created from knowledge bases | 0.80 | text |
| besides textual features generated from input documents or text corpora | instance of | Graph-basedModern entity linking systems also use large knowledge graphs created from knowledge bases | 0.80 | text |
| Entity linking | has application | Entity | 0.60 | section |
| Entity linking | has application | In | 0.60 | section |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.