Call for Papers

Call for Papers

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

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

NEWS: Accepted papers will be published through Springer LNCS

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.

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).

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. The proceedings will be published after the workshop throughLecture Notes in Computer Science Series (LNCS) by Springer together with 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.

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

Programme committee:
Caroline Barriere, CRIM
Christian Bizer, University of Mannheim
Volha Bryl, Springer Nature
Paul Buitelaar, Insight – National University of Ireland, Galway
Philipp Cimiano, Bielefeld University
Agata Filipowska, Department of Information Systems, Poznan University of Economics
Jorge Gracia, Ontology Engineering Group. Universidad Politécnica de Madrid
Anja Jentzsch, Hasso Plattner Institut
John P. Mccrae, National University of Ireland, Galway
Andrea Moro, Sapienza, Università di Roma
Roberto Navigli, Sapienza Universita’ di Roma
Giuseppe Rizzo, ISMB
Harald Sack, Hasso-Plattner-Institute for IT Systems Engineering, University of Potsdam
Felix Sasaki, W3C
Ricardo Usbeck, University of Leipzig
Sebastian Walter, CITEC, Bielefeld University
Krzysztof Wecel, Poznan University of Economics

Advertisements