JRC-Names: Multilingual Entity Name variants and titles as Linked Data

Tracking #: 1307-2519

Authors: 
Maud Ehrmann
Guillaume Jacquet
Ralf Steinberger

Responsible editor: 
Philipp Cimiano

Submission type: 
Dataset Description
Abstract: 
Since 2004 the European Commission's Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities (persons and organisations) and their many name variants. The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Benjamin/Binyamin/Bibi/Benyamín/Biniamin/Беньямин/بنيامين Netanyahu/Netanjahu/Nétanyahou/Netahny/Нетаньяху/نتنياهو). This entity name variant data, known as JRC-Names, has been available for public download since 2011. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. cross-lingual mapping, and web-based content processing, e.g. entity linking. JRC-Names is publicly available through the dataset catalogue of the European Union's Open Data Portal.
Full PDF Version: 
Tags: 
Reviewed

Decision/Status: 
Accept

Solicited Reviews:
Click to Expand/Collapse
Review #1
By John McCrae submitted on 17/Feb/2016
Suggestion:
Accept
Review Comment:

All of my comments have been met and this paper has no significant further errors. I have no further comments.

Review #2
By Jorge Gracia submitted on 22/Feb/2016
Suggestion:
Accept
Review Comment:

Very interesting and useful work. The authors have addressed the last reviewers' comments well and I find this work suitable for publication. Further, they followed the recommendation of publishing their dataset in datahub.io (although I didn't not find reference to it the text, maybe it would be a good idea to include the link in the final version).


Comments