[DL] CFP: Linked Data for Information Extraction LD4IE2016 - workshop at @ISWC2016

Claudia d'Amato claudia.damato at uniba.it
Thu Jun 16 14:49:16 CEST 2016



Apologies for multiple posting.

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

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/Ld4ie2014)


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

	Abstract submission deadline:         July 1, 2016

	Paper submission deadline              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) modeling 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 (http://webdatacommons.org/structureddata)

** work reusing the training and evaluation dataset from our past IE challenge 
(https://github.com/anuzzolese/oke-challenge-2016#task-3)

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

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) initiative. Example papers using LNCS and ACM: dokieli 
(http://csarven.ca/dokieli) and on website: https://dokie.li.

** Research Articles in Simplified HTML (RASH) format (https://github.com/essepuntato/rash): with 
the additional stylesheets and scripts included in the style package 
(http://cs.unibo.it/save-sd/rash/rash.zip) for guaranteeing a correct visualisation of the document 
on browsers. The documentation of the format is also available online 
(http://cs.unibo.it/save-sd/rash/documentation/index.html).

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 at informatik.uni-mannheim.de
office: +49 621 181 2646
skype: anlige

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