[open-linguistics] [CfP] Deadline Extension NLP&DBpedia Workshop 2016 - Proceedings published by Springer LNCS
Heiko Paulheim
heiko at informatik.uni-mannheim.de
Thu Jun 23 06:23:21 UTC 2016
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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)
http://iswc2016.semanticweb.org/
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 at easychair.org
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Motivation
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
http://archive.ics.uci.edu/ml/datasets.html).
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.
NLP4DBpedia
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.
DBpedia4NLP
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.
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Topics
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
resolution
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:
http://en.wikipedia.org/wiki/Natural_language_processing#Major_tasks_in_NLP
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).
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Submission
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
(http://keki2016.linguistic-lod.org/).
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.
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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
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Organising Committee:
Heiko Paulheim, University of Mannheim
Marieke van Erp, Vrije Universiteit Amsterdam
Pablo N. Mendes, IBM Research, USA
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