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[CfP]: Linked Data for Information Extraction LD4IE2017 - workshop at @ISWC2017

From: annalisa gentile <annalisa.gentile@ibm.com>
Date: Sat, 6 May 2017 13:30:51 -0700
To: semantic-web@w3.org
Message-Id: <OFDD6930B4.5026E021-ON00258118.00707145-88258118.0070B061@notes.na.collabserv.com>

LD4IE 2017
The 5th international Workshop on Linked Data for Information Extraction
in conjunction with the 16th International Semantic Web Conference (ISWC
2017)
Vienna, Austria, October 21-25, 2017
http://iswc2017.semanticweb.org/


Workshop website: http://w3id.org/ld4ie

Twitter: @LD4IE #LD4IE #LD4IE2017
Facebook page: Ld4ie2017 (at https://www.facebook.com/Ld4ie)

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

	Abstract submission deadline:		July 14, 2017
	Paper submission deadline:		July 21, 2017
	Acceptance Notification:			August 24, 2017
 	Camera-ready versions:			September 1, 2017
 	Workshop date:				to be announced (October 21-22,
2017)


*************** 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:

Topics
* Modelling Extraction Tasks
** extracting knowledge patterns for task modelling
** 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, …)
** Domain specific IE consuming and producing LOD (social data, scholarly
data, health data, ...)
** 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

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

All submissions must be written in English.
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.

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: 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=ld4ie2017

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, IBM Research Almaden, US
Andrea Giovanni Nuzzolese, STLab, ISTC-CNR, Italy
Ziqi Zhang, Nottingham Trent University, UK

--
Anna Lisa Gentile
IBM Research Almaden
w3id.org/people/annalisa
Received on Saturday, 6 May 2017 20:33:38 UTC

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