Submission Deadline: 7 July 2014
Notification of Acceptance: 30 July 2014
Submissions via: https://www.easychair.org/conferences/?conf=nlpdbpedia2014
More reasons to submit to NLP & DBpedia’14: the 2 best workshop papers will be nominated and invited to submit an extended version to Journal of Data Semantics!
The DBpedia community has recently experienced an immense increase in activity. We believe that the time has come to explore the connection between DBpedia & Natural Language Processing (NLP) in a yet unprecedented depth.
DBpedia has a long-standing tradition to provide useful data as well as a commitment to reliable Semantic Web technologies and living best practices. With the rise of WikiData, DBpedia is step-by-step relieved from the tedious extraction of data from Wikipedia’s infoboxes and can shift its focus on new challenges such as extracting information from the unstructured article text as well as becoming a testing ground for multilingual NLP methods.
The central role of Wikipedia (and therefore DBpedia) for the creation of a Translingual Web has recently been recognized by the Strategic Research Agenda (http://www.meta-net.eu/vision/reports/meta-net-sra-version_1.0.pdf cf. section 3.4, page 23) and most of the contributions of the recent Dagstuhl seminar on the Multilingual Semantic Web (http://www.dagstuhl.de/de/programm/kalender/semhp/?semnr=12362) also stress the role of Wikipedia for Multilingualism (http://drops.dagstuhl.de/opus/volltexte/2013/3788/pdf/dagrep_v002_i009_p015_s12362.pdf). 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 (http://linguistics.okfn.org/resources/llod/) as well as localized LOD clouds with specialized domains (e.g. the Dutch windmill domain ontology created from http://nl.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.
We envision the workshop to produce the following items:
- an open call to the DBpedia data consumer community will generate a wish list of data, which is to be generated from Wikipedia by NLP methods. This wish list will be broken down to tasks and benchmarks, and a 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).
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, list pages, 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.
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 looking to 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.
- Novel applications using the extracted knowledge, the Web of Data or NLP DBpedia-based methods.
Topics include, but are not limited to
- Improving DBpedia with NLP methods
- Finding errors in 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
- Disambiguation through knowledge base
- Ontology representation of natural language text
- 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).
The workshop will be pro-active to encourage collaborative participation: for example, live minutes of the workshop will be taken using an open EtherPad. We plan to collect the material used by each submission such as dataset used, source code, etc. and to share it to the whole community using a portal such as CKAN. Moreover, we intend to give to the attendees a big picture from the workshop day and to mainly discuss and fill the topics highlighted in the Knowledge Extraction Wikipedia page. Participants are also encouraged to extend the Wikipedia page.
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 CEUR-WS.
We welcome the following types of contributions:
- Full research papers (up to 12 pages).
- Position papers (up to 6 pages)
- Use case descriptions (up to 6 pages)
- Data/benchmark papers (2-6 pages, depending on the size and complexity)
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=nlpdbpedia2014
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 2014 submissions are not anonymous.
– submission date: 7 July, 2014, 23:59 Hawaii time
– author notifications: July 30, 2014, 23:59 Hawaii time
– camera-ready: August 20, 2014, 23:59 Hawaii time
– NLP & DBpedia 2014: October 19 or 20, 2014
- Heiko Paulheim, University of Mannheim
- Marieke van Erp VU University Amsterdam
- Agata Filipowska, Poznan University of Economics and I2G, Poznan
- Pablo N. Mendes, IBM Research, USA
- Guadalupe Aguado, Universidad Politécnica de Madrid, Spain
- Christian Bizer, Universität Mannheim, Germany
- Volha Bryl, Universität Mannheim, Germany
- Martin Brümmer, Universität Leipzig, Germany
- Paul Buitelaar, DERI, National University of Ireland, Galway
- Philipp Cimiano, CITEC, Universität Bielefeld, Germany
- Jorge Gracia, Universidad Politécnica de Madrid, Spain
- Sebastian Hellmann, DBpedia Association, Germany
- Anja Jentzsch, Hasso-Plattner-Institut, Potsdam, Germany
- Dimitris Kontokostas, Universität Leipzig, Germany
- John McCrae, Universität Bielefeld, Germany
- Roberto Navigli, Sapienza, Università di Roma, Italy
- Simone Paolo Ponzetto, University of Mannheim
- Giuseppe Rizzo, Università di Torino, Italy
- Felix Sasaki, Deutsches Forschungszentrum für künstliche Intelligenz, Germany
- Ricardo Usbeck, AKSW, Universität Leipzig, Germany
- Rupert Westenthaler, Salzburg Research, Austria
- Feiyu Xu, Deutsches Forschungszentrum für künstliche Intelligenz, Germany