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Apologies for multiple posting.
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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/>http://iswc2016.semanticweb.org/
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Workshop
website:<http://web.informatik.uni-mannheim.de/ld4ie2016>http://web.informatik.uni-mannheim.de/ld4ie2016
Twitter: @LD4IE #LD4IE #LD4IE2016
Facebook page: Ld4ie2016
(at<https://www.facebook.com/Ld4ie2014>https://www.facebook.com/Ld4ie2014)
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*************** Important Dates ***************
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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)
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*************** Call for Papers ***************
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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.
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*************** Research Topics ***************
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Topics of interest include, but are **not limited to**:
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* 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
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* 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
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* 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
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* Special interest: IE using Web Data Commons corpus
** any IE tasks using (part of) the Web Data Commons corpus
(<http://webdatacommons.org/structureddata>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>https://github.com/anuzzolese/oke-challenge-2016#task-3)
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*************** Submission ********************
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We accept the following formats of submissions:
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* Full paper with a maximum of 12 pages including references
* Short paper with a maximum of 6 pages including references
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Two formats are possible for the submission: PDF and HTML.
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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>http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.).
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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>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: dokieli
(<http://csarven.ca/dokieli>http://csarven.ca/dokieli) and on
website:<https://dokie.li>https://dokie.li.
** Research Articles in Simplified HTML (RASH) format
(<https://github.com/essepuntato/rash>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>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>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.
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Please submit your contributions electronically in PDF or HTML format to
EasyChair
at<https://www.easychair.org/conferences/?conf=ld4ie2016>https://www.easychair.org/conferences/?conf=ld4ie2016
When submitting your paper, select the appropriate topic between:
* Research - long paper
* Research - short paper
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Accepted papers will be published online via CEUR-WS.
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*************** Workshop Chairs ***************
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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
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Anna Lisa Gentile
Postdoctoral Researcher
Data and Web Science Group
University of Mannheim
https://w3id.org/people/annalisa
email:annal...@informatik.uni-mannheim.de
office: +49 621 181 2646
skype: anlige
