News Story

WOSP2017 - Touchdown Toronto

David Pride, Wednesday 19 July 2017 | Annotate

Since 2012, members of KMi’s CORE team, headed by Petr Knoth, have orchestrated the WOSP (Workshop On mining Scientific Publications) held each year as a part of JCDL (Joint Conference on Digital Libraries.

Previously held in locations as diverse as London and Indianapolis, this year the 6th annual international WOSP workshop took place at the University of Toronto. Over 100 academics joined us to hear presentations from 14 authors on a wide range of topics, from using machine learning to detect academic plagiarism, to using text and data-mining to interrogate a bilingual scientific repository. Our very own Petr Knoth presented new research on Recommender Systems, you can see the slides from this presentation HERE. We also had several demonstrations; Victor Botev gave us a really nice overview of their ‘Iris.ai – the Science Assistant’ project whilst Ron Daniel from Elsevier presented the ‘Content Analytics Toolbench (CAT)’

Jevin West - Keynote                                      Jevin West                                                                       Waleed Ammar

We had fantastic keynote speeches, from Jevin West (Assistant Professor at the Information School at the University of Washington and co-director of the DataLab ) who introduced us to VizioMetrix, a platform that extracts visual information from the scientific literature. Our second keynote was Waleed Ammar, research team lead at Semantic Scholar, who spoke about their latest work around citation extraction and recommender systems. Researchers can check out their Cite-o-Matic recommender HERE.
 
Many thanks to all our speakers, authors and attendees, hopefully we’ll see many of you next year for WOSP 2018!

 



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Petr Knoth

Senior Research Fellow in Text and Data Mining

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CORE

Connecting REpositories by automatically linking Open Access research publications

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CORE - COnnecting REpositories

Linking semantically similar publications from Open Access repositories using text mining from full-text and representing the relations as Linked Data

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