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[protege-discussion] **DEADLINE EXTENSION** - LD4IE @ISWC2013: Linked Data for Information Extraction

Claudia d'Amato claudia.damato at
Thu Jul 11 02:04:00 PDT 2013


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

Workshop website:
Twitter: @LD4IE2013 #LD4IE #LD4IE2013
Facebook page:

in conjunction with

ISWC 2013
The 12th International Semantic Web Conference
Sydney, Australia, October 21 -25, 2013

*************** 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 Extraction…)
* 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 ( Please submit your 
contributions electronically in PDF format to EasyChair at

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
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