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[CFP] DEADLINE EXTENSION: Linked Data for Information Extraction LD4IE2016 at @ISWC2016

From: Anna Lisa Gentile <annalisa@informatik.uni-mannheim.de>
Date: Fri, 1 Jul 2016 16:30:58 +0200
To: semantic-web@w3c.org
Message-ID: <b76d8920-c0b0-714c-c614-83c7dccd5f02@informatik.uni-mannheim.de>
**

*LD4IE 2016: DEADLINE EXTENDED!!!!LD4IE 2016*

*

The 4th international Workshop on Linked Data for Information Extraction

in conjunction with The 14th International Semantic Web Conference (ISWC 
2016)

Kobe, Japan October 17-21, 2016

http://iswc2016.semanticweb.org/


Workshop website: http://web.informatik.uni-mannheim.de/ld4ie2016

Twitter: @LD4IE #LD4IE #LD4IE2016

Facebook page: Ld4ie2016 (at https://www.facebook.com/Ld4ie)

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


Abstract submission deadline:July 7, 2016 (extended from July 1, 2016)

Paper submission deadline:July 15, 2016 (extended from July 7, 2016)

Acceptance Notification:July 31, 2016

Camera-ready versions:August 7, 2016

Workshop date:to be announced (17-21 October 2016)



*************** 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.); (ii) robust 
learning algorithms for handling LD; (iv) publishing IE results to the 
LOD cloud.


*************** Research 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

**** IE tasks/applications exploiting LD (Wrapper induction, Table 
interpretation, IE from unstructured data, Named Entity Recognition, 
Relation Extraction, Topic Modelling…)

**** 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 [1]

**** work reusing the training and evaluation dataset from our past IE 
challenge [2]

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

[2] https://github.com/anuzzolese/oke-challenge-2016#task-3


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


We accept the following formats of submissions:

Research Papers

---------------

Full paper with a maximum of 12 pages including references.

Short paper with a maximum of 6 pages including references.

Two formats are possible for the submission: PDF and HTML.


All submissions must be written in English.

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:

- dokieli (https://github.com/linkeddata/dokieli) is a clientside editor 
for decentralised article publishing, annotations and social 
interactions. It is compliant with the Linked Research 
(<https://linkedresearch.org/>https://linkedresearch.org/) initiative. 
Example papers using LNCS and ACM: 
<http://csarven.ca/dokieli>http://csarven.ca/dokieli and on website 
https://dokie.li/.

- Research Articles in Simplified HTML (RASH) format: documentation and 
stylesheets at https://github.com/essepuntato/rash


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 at https://www.easychair.org/conferences/?conf=ld4ie2016

When submitting your paper, select the appropriate topic between:

* Research - long paper

* Research - short paper


Accepted papers will be published online via CEUR-WS.


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


Anna Lisa Gentile, University of Mannheim, Germany

Claudia d'Amato, University of Bari, Italy

Ziqi Zhang, University of Sheffield, UK

Heiko Paulheim, University of Mannheim, Germany

*

-- 
Anna Lisa Gentile
Postdoctoral Researcher
Data and Web Science Group
University of Mannheim
https://w3id.org/people/annalisa
email:annalisa@informatik.uni-mannheim.de
office: +49 621 181 2646
skype: anlige
Received on Friday, 1 July 2016 14:38:22 UTC

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