Apologise for multiple posting.
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LD4IE 2013
The 1st international Workshop on Linked Data for Information Extraction
Sidney, Australia, October 21 -22, 2013
Workshop website:http://oak.dcs.shef.ac.uk/ld4ie2013/index.html
Twitter: <at> LD4IE2013 #LD4IE #LD4IE2013
Facebook page:https://www.facebook.com/Ld4ie2013
in conjunction with
ISWC 2013
The 12th International Semantic Web Conference
Sidney, Australia, October 21 -25, 2013
http://iswc2013.semanticweb.org/
************************* 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
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 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
* modeling extraction tasks
* extracting knowledge patterns for task modeling
* user friendly approaches for querying linked data
***** Information Extraction
* selecting relevant portions of LOD as training data
* selecting relevant knowledge resources from linked data
* IE methods robust to noise in training data
* Information Extractions tasks/applications exploiting LOD (Wrapper
induction, Table
interpretation, IE from unstructured data, Named Entity Recognition...)
* publishing information extraction results as Linked Data
* linking extracted information to existing LOD datasets
***** Linked Data for Learning
* assessing the quality of LOD data for training
* select optimal subset of LOD to seed learning
* managing incompleteness, noise, and uncertainty of LOD
* scalable learning methods
* pattern extraction from LOD
************************* Important Dates*************************
Abstract submission deadline: July 5, 2013
Paper submission deadline: July 12, 2013
Acceptance Notification: August 9, 2013
Camera-ready versions: to be announced
Workshop date: to be announced (21-22 October
2013)
************************* 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
athttps://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
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