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). Introduction 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

Official source

https://en.wikipedia.org/wiki/Entity_linking

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idMesh: graph-based disambiguation of linked data

Karl Aberer, Philippe Cudré-Mauroux, Parisa Haghani, Michael Jost

We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities -- represented by globally identifiable declarative artifacts -- self-organize in a dynamic and probabilistic manner. Our solution has the following two desirable properties: i) it lets end-users freely define associations between arbitrary entities and ii) it probabilistically infers entity relationships based on uncertain links using constraint-satisfaction mechanisms. We outline the interface between our scheme and the current data Web, and show how higher-layer applications can take advantage of our approach to enhance search and update of information relating to online entities. We describe a decentralized infrastructure supporting efficient and scalable entity disambiguation and demonstrate the practicability of our approach in a deployment over several hundreds of machines.

ACM2009

TweetSpector: Entity-based retrieval of Tweets

Karl Aberer, Flavia Adelina Grosan, Zoltán Miklós, Alexandru Octavian Tandrau, Surender Reddy Yerva

People readily express their opinions about the various products, companies, TV shows etc., on Twitter. These tweet messages are thus a rich source of information that can be exploited to understand the sentiments about the concerned products or services. Retrieving the tweets related to given entities is however a challenging task as their names are often (deliberately) ambiguous, e.g. Apple, Blackberry, Friends, etc. Nevertheless, identifying the relevant entities is an essential first step to develop reliable sentiment analysis techniques that is not considered in existing systems, for example: TweetFeel(http://www.tweetfeel.com), TwitterSentiment(http://twittersentiment.appspot.com) While there is a number of techniques for identifying named entities in unstructured text, they are often not directly applicable in this case, as tweet messages are very short (maximal 140 characters). This demonstrator introduces TweetSpector, a tool that addresses this retrieval task and enables to link tweet messages to a given entity. Our retrieval methods rely on classification techniques that exploit our concise descriptions of entity-relevant information, also called entity profiles.

2012

Entity Disambiguation in Tweets leveraging User Social Profiles

Karl Aberer, Michele Catasta, Surender Reddy Yerva

2013