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CFP: LD4IE2014 Linked Data for Information Extraction - ISWC2014 workshop

From: Anna Lisa Gentile <a.l.gentile@dcs.shef.ac.uk>
Date: Thu, 01 May 2014 09:45:53 +0100
Message-ID: <536209C1.5090909@dcs.shef.ac.uk>
To: semantic-web@w3c.org

Apologies for multiple posting.

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

LD4IE 2014
The 2nd international Workshop on Linked Data for Information Extraction
Riva del Garda - Trentino, Italy, October 19-20, 2014

Workshop website: http://oak.dcs.shef.ac.uk/ld4ie2014
Twitter: @LD4IE2014 #LD4IE #LD4IE2014
Facebook page: https://www.facebook.com/Ld4ie2014

in conjunction with

ISWC 2014
The 13th International Semantic Web Conference
Riva del Garda - Trentino, Italy, October 19-23, 2014
http://iswc2014.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 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.

As the second workshop of this series, a special focus this year will be 
on using Webpages embedded with structured data describing products, 
people, organizations, places, events using markup standards such as 
RDFa, Microdata and Microformats. We especially encourage the use of the 
Web Data Commons corpus of structured data [1] for experiments. The 
dataset contains billions of triples in terrabytes of data. Unlike the 
Billion Triple Challenges that typically contain triples from specific 
linked data sets such as DBpedia, the Web Data Commons corpus consists 
of specifically triples extracted from Webpages annotated with standard 
markup vocabularies such as RDFa, Microdata format. The provenance of 
triples is also recorded so the original Webpages containing those 
annotations can be obtained. Although it is not strictly required to use 
this corpora, submissions that do use this (or part of) corpora will be 
considered for extra credits. Authors may use this data for any 
IE-related tasks, although we may define some specific 'example' tasks 
in later calls to invite participants.

[1] http://webdatacommons.org/structureddata/

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

*** Special interest: IE using Web Data Commons corpus
* any IE tasks using (part of) the Web Data Commons corpus

*************** Important Dates ***************

Abstract submission deadline: June 30, 2014
Paper submission deadline: July 7, 2014
Acceptance Notification: July 30, 2014
Camera-ready versions: August 20, 2014
Workshop date: to be announced (19-20 October 2014)


*************** 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
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=ld4ie2014

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 Thursday, 1 May 2014 08:46:21 UTC

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