Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities . Yet for many types of entities , such as restaurants and cult movies , relational databases exist that contain far more extensive information than Wikipedia . This paper introduces a new task , called Open-Database Named-Entity Disambiguation ( Open-DB NED ) , in which a system must be able to resolve named entities to symbols in an arbitrary database , without requiring labeled data for each new database . We introduce two techniques for Open-DB NED , one based on distant supervision and the other based on domain adaptation . In experiments on two domains , one with poor coverage by Wikipedia and the other with near-perfect coverage , our Open-DB NED strategies outperform a state-of-the-art Wikipedia NED system by over 25 % in accuracy . Named-entity disambiguation ( NED ) is the task of linking names mentioned in text with an established catalog of entities ( Bunescu and Pasca , 2006 ; Ratinov et al . , 2011 ) . It is a vital first step for semantic understanding of text , such as in grounded semantic parsing ( Kwiatkowski et al . , 2011 ) , as well as for information retrieval tasks like person name search ( Chen and Martin , 2007 ; Mann and Yarowsky , 2003 ) . NED requires a catalog of symbols , called referents , to which named-entities will be resolved . Most NED systems today use Wikipedia as the catalog of referents , but exclusive focus on Wikipedia as a target for NED systems has significant drawbacks : despite its breadth , Wikipedia still does not contain all or even most real-world entities mentioned in text . As one example , it has poor coverage of entities that are mostly important in a small geographical region , such as hotels and restaurants , which are widely discussed on the Web . 57 % of the named-entities in the Text Analysis Conference 's ( TAC ) 2009 entity linking task refer to an entity that does not appear in Wikipedia ( McNamee et al . , 2009 ) . Wikipedia is clearly a highly valuable resource , but it should not be thought of as the only one . Instead of relying solely on Wikipedia , we propose a novel approach to NED , which we refer to as Open-DB NED : the task is to resolve an entity to Wikipedia or to any relational database that meets mild conditions about the format of the data , described below . Leveraging structured , relational data should allow systems to achieve strong accuracy , as with domain-specific or database-specific NED techniques like Hoffart et al . 's NED system for YAGO ( Hoffart et al . , 2011 ) . And because of the availability of huge numbers of databases on the Web , many for specialized domains , a successful system for this task will cover entities that a Wikipedia NED or database-specific system can not . We investigate two complementary learning strategies for Open-DB NED , both of which significantly relax the assumptions of traditional NED systems . The first strategy , a distant supervision approach , uses the relational information in a given database and a large corpus of unlabeled text to learn a database-specific model . The second strat-egy , a domain adaptation approach , assumes a single source database that has accompanying labeled data . Classifiers in this setting must learn a model that transfers from the source database to any new database , without requiring new training data for the new database . Experiments show that both strategies outperform a state-of-the-art Wikipedia NED system by wide margins without requiring any labeled data from the test domain , highlighting the significant advantage of having domain-specific relational data . The next section contrasts Open-DB NED with previous work . Section 3 formalizes the task . Sections 4 and 5 present our distant supervision strategy and domain-adaptation strategy , respectively . Section 6 introduces a technique that is a hybrid of the two learning strategies . Section 7 describes our experiments , and Section 8 concludes . This paper introduces the task of Open-DB Named Entity Disambiguation , and presents two distinct strategies for solving this task . Experiments indicate that a mixture of the two strategies significantly outperforms a state-of-the-art Wikipedia NED system , on a dataset where Wikipedia has good coverage and on another dataset where Wikipedia has poor coverage . The results indicate that there is a significant benefit to leveraging other sources of knowledge in addition to Wikipedia , and that it is possible to leverage this knowledge without requiring labeled data for each new source . The initial success of these Open-DB NED approaches indicates that this task is a promising area for future research , including exciting extensions that link large numbers of domainspecific databases to text .
Existing approaches to disambiguate named entities solely use Wikipedia as a catalogue however Many kinds of named entities are missed in Wikipedia.
They propose a task where systems need to reference arbitrary databases for finding named entities not only Wikipedia, together with methods to achieve domain adaptation.
A mixture of two domain adaptation methods outperforms existing systems that only rely on Wikipedia for their new Open-DB Named Entity Disambiguation task.