LD4IE 2017, is the fifth international workshop on Linked Data for Information Extraction, co-located with ISWC2017.
LD4IE past editions:
LD4IE focuses on the exploitation of Linked Data for Web Scale Information Extraction (IE), which concerns extracting structured knowledge from unstructured/semi-structured documents on the Web. One of the major bottlenecks for the current state of the art in IE is the availability of learning materials (e.g., seed data, training corpora), which, typically are manually created and are expensive to build and maintain. Linked Data (LD) defines best practices for exposing, sharing, and connecting data, information, and knowledge on the Semantic Web using uniform means such as URIs and RDF. It has so far been created a gigantic knowledge source of Linked Open Data (LOD), which constitutes a mine of learning materials for IE. However, the massive quantity requires efficient learning algorithms and the unguaranteed quality of data requires robust methods to handle redundancy and noise. LD4IE intends to gather researchers and practitioners to address multiple challenges arising from the usage of LD as learning material for IE tasks, focusing on (i) modelling user defined extraction tasks using LD; (ii) gathering learning materials from LD assuring quality (training data selection, cleaning, feature selection etc.); (iii) robust algorithms for various IE tasks using LD; (iv) publishing IE results to the LOD cloud.
All submissions must be written in English. We accept the following formats of submissions:
Two formats are possible for the submission: PDF and HTML. PDF submissions must be formatted according to the information for LNCS Authors (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.). We would like to encourage you to submit your paper as HTML, in which case you need to submit a zip archive containing an HTML file and all used resources. If you are new to HTML submission these are good places to start:
In order to check if your HTML submission is compliant with the page limit constraint, please use one of the LNCS layouts and printing/storing it as PDF. Please submit your contributions electronically in PDF or HTML format to EasyChair Accepted papers will be published online via CEUR-WS.
15:20-16:00 Coffee Break
Program Committee Rabeeh Ayaz Abbasi,King Abdulaziz University, Jeddah, Saudi Arabia Nitish Aggarwal, IBM Research Almaden, CA, US Payam Barnaghi, University of Surrey Pierpaolo Basile, University of Bari Amparo E. Cano, Data Scientist at Cube Global Annalina Caputo, ADAPT - School of Computer Science and Statistics, Trinity College Dublin Claudia d'Amato, University of Bari Mauro Dragoni, Fondazione Bruno Kessler - FBK-IRST Anca Dumitrache, VU University Amsterdam Darío Garigliotti, University of Stavanger Ashutosh Jadhav, IBM Research Almaden, CA, US Petr Knoth, KMi, The Open University Vanessa Lopez , IBM Research Andrea Moro, Microsoft, London Varish Mulwad, GE Global Research Matthias Nickles, National University of Ireland, Galway, Digital Enterprise Research Institute Jay Pujara, University of California, Santa Cruz Achim Rettinger, Karlsruhe Institute of Technology Martin Rezk, Rakuten, Inc. Giuseppe Rizzo, ISMB Mariano Rodríguez Muro, IBM Research Victoria Uren, Aston University