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[CfP] Deadline Extension NLP&DBpedia Workshop 2016 - Proceedings published by Springer LNCS

From: Heiko Paulheim <heiko@informatik.uni-mannheim.de>
Date: Thu, 23 Jun 2016 08:23:22 +0200
To: semantic-web@w3.org
Message-ID: <1a316b19-96d7-dc4d-fc77-98c6a9fdd7dc@informatik.uni-mannheim.de>
        3rd International Workshop on NLP & DBpedia 2016
       NEWS: Accepted papers will be published through Springer LNCS

                        17 or 18 October 2016
                              Kobe, Japan
Collocated with the 15th International Semantic Web Conference (ISWC2016)

Submission Deadline: 7 July 2016
Notification of Acceptance: 31 July 2016

Workshop URI: https://nlpdbpedia2016.wordpress.com/
Submissions via: https://easychair.org/conferences/?conf=nlpdbpedia2016
Hashtag: #NLPDBP2016
Contact: nlpdbpedia2016@easychair.org

The central role of Wikipedia (and therefore DBpedia) for the creation 
of a Translingual Web has been recognized by the Strategic Research 
Agenda (cf. section 3.4, page 23) and most of the contributions of the 
Dagstuhl seminar on the Multilingual Semantic Web also stress the role 
of Wikipedia for Multilingualism. The previous editions of the 
NLP&DBpedia workshop also contribute to this understanding.

As more and more language-specific chapters of DBpedia are created 
(currently 14 language editions), DBpedia is becoming a driving factor 
for a Linguistic Linked Open Data cloud as well as localized LOD clouds 
with specialized domains (e.g. the Dutch windmill domain ontology 
created from http://nl.dbpedia.org or Japanese domain ontology of screws 
from http://ja.dbpedia.org/).

The data contained in Wikipedia and DBpedia have ideal properties for 
making them a controlled testbed for NLP. Wikipedia and DBpedia are 
multilingual and multi-domain, the communities maintaining these 
resource are very open and it is easy to join and contribute. The open 
licence allows data consumers to benefit from the content and many parts 
are collaboratively editable. Especially, the data in DBpedia is widely 
used and disseminated throughout the Semantic Web.

With the foundation of the DBpedia Association and the frequent releases 
of the DBpedia+ Data Stack, this workshop hopes to channel contributions 
of the NLP research community into the data ecosystem of DBpedia and 
LOD, thus easing the use of interlinked language resources as well as 
increasing the performance of knowledge-based NLP approaches.

We envision the workshop to produce the following items:
an open call to the DBpedia data consumer community that will generate a 
wish list of data, which is to be generated from Wikipedia using NLP 
methods (for certain domains and application scenarios). This wish list 
will be broken down to tasks and benchmarks and as a result GOLD 
standard will be created.
the benchmarks and test data created will be collected and published 
under an open licence for future evaluation (inspired by 
http://oaei.ontologymatching.org/ and 
strengthen the link between DBpedia and NLP communities that currently 
meet two times a year at DBpedia developers workshops.
We also offer all authors the chance to contribute their data to the 
regular DBpedia releases in April and October.
DBpedia has been around for quite a while, infusing the Web of Data with 
multi-domain data of decent quality. The data in DBpedia is, however, 
mostly extracted from Wikipedia infoboxes, while the remaining parts of 
Wikipedia are to a large extent not exploited for DBpedia. Here, NLP 
techniques may help improving DBpedia.

Extracting additional triples from the plain text information in 
Wikipedia, either unsupervised or using the existing triples as training 
information, could multiply the information in DBpedia, or help telling 
correct from incorrect information by finding supporting text passages. 
Furthermore, analyzing the semantics of other structures in Wikipedia, 
such as tables, lists, or categories, would help make DBpedia richer. 
Finally, since Wikipedia exists in more than 200 languages, we are 
particularly interested in seeing NLP approaches not only working for 
English, but also for other languages, in order to leverage the huge 
amount of knowledge captured in the different language editions.

NLP approaches enable also improving quality of DBpedia, especially by 
extracting content from sources other than Wikipedia that may validate 
the data in DBpedia.
On the other hand, NLP and information extraction techniques often 
involve various resources while processing texts from different domains. 
As high-quality annotated data is often too expensive and time-consuming 
to obtain, NLP researchers are increasingly looking to the Semantic Web 
for external structured sources to complement their datasets. Such 
resources can be gazetteers to aid a named entity recognition system or 
examples of relations between entities to bootstrap a relation finder. 
DBpedia can easily be utilised to assist NLP modules in a variety of tasks.

We invite papers from both these areas including:
Knowledge extraction from text and HTML documents (especially 
unstructured and semi-structured documents) on the Web, using 
information in the Linked Open Data (LOD) cloud, and especially in DBpedia.
Representation of NLP tool output and NLP resources as RDF/OWL, and 
linking the extracted output to the LOD cloud or the Linguistic LOD cloud .
Novel applications using the extracted knowledge, the Web of Data or NLP 
DBpedia-based methods.

The specific topics of the workshop are listed below.


Enhancing DBpedia with NLP methods
Finding errors in DBpedia with NLP methods
Enriching DBpedia with NLP methods
Improving quality of DBpedia with NLP methods
Annotation methods for Wikipedia articles
Cross-lingual data and text mining on Wikipedia
Pattern and semantic analysis of natural language, reading the Web, 
learning by reading
Large-scale information extraction
Entity resolution and automatic discovery of Named Entities
Multilingual entity recognition task of real world entities
Frequent pattern analysis of entities
Relationship extraction, slot filling
Entity linking, Named Entity disambiguation, cross-document co-reference 
Analysis of ontology models for natural language text
Learning and refinement of ontologies
Natural language taxonomies modeled to Semantic Web ontologies
Use cases of entity recognition for Linked Data applications
Impact of entity linking on information retrieval, semantic search

Furthermore, an informal list of NLP tasks can be found on this 
Wikipedia page: 
These are relevant for the workshop as long as they fit into the 
DBpedia4NLP  and NLP4DBpedia frame (i.e. the used data evolves around 
Wikipedia and DBpedia).


All papers must represent original and unpublished work that is not 
currently under review. Papers will be evaluated according to their 
significance, originality, technical content, style, clarity, and 
relevance to the workshop. At least one author of each accepted paper is 
expected to attend the workshop. Accepted papers will be published 
through Lecture Notes in Computer Science Series (LNCS) by Springer 
together with papers from the KeKi workshop 

We welcome the following types of contributions:
* Full research papers (up to 16 pages).
* Position papers (up to 12 pages)

All submissions must be written in English and must be formatted 
according to the style for Lecture Notes in Computer Science (LNCS) 
Authors. Please submit your contributions electronically in PDF format 
to https://www.easychair.org/conferences/?conf=nlpdbpedia2016

For details on the LNCS style, see the Springer Author Instructions at 
http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0. NLP & 
DBpedia 2016 submissions are not anonymous.

Important Dates:

- submission date: 7 July 2016, 23:59 Hawaii time
- author notifications: 31 July 2016, 23:59 Hawaii time
- pre-workshop paper:    1 September, 2016
- NLP & DBpedia 2016: 17 or 18 October 2016
- camera-ready for post-proceedings: 18 November 2016, 23:59 Hawaii time

Organising Committee:
Heiko Paulheim, University of Mannheim
Marieke van Erp, Vrije Universiteit Amsterdam
Pablo N. Mendes, IBM Research, USA
Received on Thursday, 23 June 2016 06:24:25 UTC

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