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**deadline extension** LD4IE @ISWC2013: Linked Data for Information Extraction

From: Anna Lisa Gentile <a.l.gentile@dcs.shef.ac.uk>
Date: Wed, 10 Jul 2013 23:14:33 +0100
Message-ID: <51DDDCC9.1090303@dcs.shef.ac.uk>
To: semantic-web@w3c.org
Apologies for multiple posting.
Deadline extension: submission accepted until the 19th of July, please 
submit your abstract as soon as possible!

***********************************************

LD4IE 2013
The 1st international Workshop on Linked Data for Information Extraction
Sydney, Australia, October 21 -22, 2013

Workshop website: http://oak.dcs.shef.ac.uk/ld4ie2013/index.html
Twitter: @LD4IE2013 #LD4IE #LD4IE2013
Facebook page: https://www.facebook.com/Ld4ie2013

in conjunction with

ISWC 2013
The 12th International Semantic Web Conference
Sydney, Australia, October 21 -25, 2013
http://iswc2013.semanticweb.org/

*************** Important Dates - NEW!!! ***************

Abstract submission deadline: Submit your abstract ASAP!
Paper submission deadline: July 19, 2013
Acceptance Notification: August 9, 2013
Camera-ready versions: to be announced
Workshop date: to be announced (21-22 October 2013)

*************** Call for Papers ***************

This workshop 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 not guaranteed 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 learning algorithms for handling LD; (iv) publishing IE results 
to the LOD cloud.

*************** Topics ************************

Topics of interest include, but are not limited to:

*** Modelling Extraction Tasks
* modelling extraction tasks (e.g. defining IE templates using LD 
ontologies)
* extracting and building knowledge patterns based on LD
* user friendly approaches for querying LD
*** Information Extraction
* selecting relevant portions of LD as training data
* selecting relevant knowledge resources from LD
* IE methods robust to noise in LD as training data
* Information Extractions tasks/applications exploiting LD (Wrapper 
induction, Table interpretation, IE from unstructured data, Named Entity 
Recognition, Relation Extractionů)
* linking extracted information to existing LD datasets

*** Linked Data for Learning
* assessing the quality of LD data for training
* select optimal subset of LD to seed learning
* managing heterogeneity, incompleteness, noise, and uncertainty of LD
* scalable learning methods using LD
* pattern extraction from LD



*************** Submission ********************

We accept the following formats of submissions:

Full paper with a maximum of 12 pages including references
Short paper with a maximum of 6 pages including references
Poster with a maximum of 4 pages including references
All submissions must be written in English and must be formatted 
according to the information for LNCS Authors 
(http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.). Please 
submit your contributions electronically in PDF format to EasyChair at 
https://www.easychair.org/conferences/?conf=ld4ie

Accepted papers will be published online via CEUR-WS.


*************** Workshop Chairs ***************

Anna Lisa Gentile, University of Sheffield, UK
Ziqi Zhang, University of Sheffield, UK
Claudia d'Amato, University of Bari, Italy
Heiko Paulheim, University of Mannheim, Germany

-- 
Anna Lisa Gentile
Research Associate
Department of Computer Science
University of Sheffield
http://staffwww.dcs.shef.ac.uk/people/A.L.Gentile
office: +44 (0)114 222 1876
Received on Wednesday, 10 July 2013 22:15:10 UTC

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