Purpose – The purpose of this paper is to focus on the problem of named entity disambiguation. The paper disambiguates named entities on a very detailed level. To each entity is assigned a concrete identifier of a corresponding Wikipedia article describing the entity. Design/methodology/approach – For such a fine‐grained disambiguation a correct representation of the context is crucial. The authors compare various context representations: bag of words representation, linguistic representation and structured co‐occurrence representation. Models for each representation are described and evaluated. They also investigate the possibilities of multilingual named entity disambiguation. Findings – Based on this evaluation, the structured co‐occurrence representation provides the best disambiguation results. It showed up that this method could be successfully applied also on other languages, not only on English. Research limitations/implications – Despite its good results the structured co‐occurrence context representation has several limitations. It trades precision for recall, which might not be desirable in some use cases. Also it is not able to disambiguate two different types of entities, which are mentioned under the same name in the same text. These limitations can be overcome by combination with other described methods. Practical implications – The authors provide a ready‐made web service, which can be directly plugged in existing applications using a REST interface. Originality/value – The paper proposes a new approach to named entity disambiguation exploiting various context representation models (bag of words, linguistic and structural representation). The authors constructed a comprehensive dataset based on all English Wikipedia articles for named entity disambiguation. They evaluated and compared the individual context representation models on this dataset. They evaluate the support of multiple languages.
International Journal of Web Information Systems – Emerald Publishing
Published: Aug 23, 2013
Keywords: Named entity recognition; Named entity disambiguation; Semantic web; Linked data; Text annotation; Semantics; Language