I applaud this initiative to extract triples from Wikipedia open text. However,
it would be useful to initiate a parallel challenge/effort to represent a
limited portion of the current Wikipedia article text as semantic
representation, eliminating the text altogether. In this approach, the
Wikipedia information would be semantically encoded as its original
representation, as opposed to using text to represent the information. A small
subset of Wikipedia subject matter could be used for this experiment. After the
limited Wikipedia domain of interest was fully semantically represented, tools
could be developed to translate the semantic representation into human readable
text. It seems over the long run creating the original knowledge as a semantic
representation, instead of text, would result in a Wikipedia knowledge base
that upon query by humans could automatically perform the necessary translation
into text in whichever human language the user desired. This concept would also
facilitate machine to machine use of the Wikipedia knowledge base, which is
currently difficult, if not impossible, due to the textual nature of the
information. You could also envision tools that would eventually make it easy
for authors to source the article information directly in semantic
representation. The end results would be a DBpedia on steroids and the
eventually elimination of Wikipedia as the original article text sources would
no longer be needed.
John Flynn
http://semanticsimulations.com
From: Sebastian Hellmann [mailto:[email protected]]
Sent: Monday, March 06, 2017 5:56 AM
To: DBpedia
Subject: [DBpedia-discussion] DBpedia Open Text Extraction Challenge - TextExt
DBpedia Open Text Extraction Challenge - TextExt
Website: http://wiki.dbpedia.org/textext
Disclaimer: The call is under constant development, please refer to the news
section. We also acknowledge the initial engineering effort and will be lenient
on technical requirements for the first submissions and will focus evaluation
on the extracted triples and allow late submissions, if they are coordinated
with us.
Background
DBpedia and Wikidata currently focus primarily on representing factual
knowledge as contained in Wikipedia infoboxes. A vast amount of information,
however, is contained in the unstructured Wikipedia article texts. With the
DBpedia Open Text Extraction Challenge, we aim to spur knowledge extraction
from Wikipedia article texts in order to dramatically broaden and deepen the
amount of structured DBpedia/Wikipedia data and provide a platform for
benchmarking various extraction tools.
Mission
Wikipedia has become the ubiquitous source of knowledge for the world enabling
humans to lookup definitions, quickly become familiar with new topics, read up
background infos for news event and many more - even settling coffee house
arguments via a quick mobile research. The mission of DBpedia in general is to
harvest Wikipedia’s knowledge, refine and structure it and then disseminate it
on the web - in a free and open manner - for IT users and businesses.
News and next events
Twitter: Follow @dbpedia <https://twitter.com/dbpedia> , Hashtag: #dbpedianlp
<https://twitter.com/search?f=tweets&q=%23dbpedianlp&src=typd>
· LDK <http://ldk2017.org/> conference joined the challenge (Deadline
March 19th and April 24th)
· SEMANTiCS <http://2017.semantics.cc/> joined the challenge (Deadline
June 11th and July 17th)
· Feb 20th, 2017: Full example added to this website
· March 1st, 2017: Docker image (beta)
https://github.com/NLP2RDF/DBpediaOpenDBpediaTextExtractionChallenge
Coming soon:
· beginning of March: full example within the docker image
· beginning of March: DBpedia full article text and tables (currently
only abstracts) http://downloads.dbpedia.org/2016-10/core-i18n/
Methodology
The DBpedia Open Text Extraction Challenge differs significantly from other
challenges in the language technology and other areas in that it is not a one
time call, but a continuous growing and expanding challenge with the focus to
sustainably advance the state of the art and transcend boundaries in a
systematic way. The DBpedia Association and the people behind this challenge
are committed to provide the necessary infrastructure and drive the challenge
for an indefinite time as well as potentially extend the challenge beyond
Wikipedia.
We provide the extracted and cleaned full text for all Wikipedia articles from
9 different languages in regular intervals for download and as Docker in the
machine readable NIF-RDF <http://persistence.uni-leipzig.org/nlp2rdf/> format
(Example for Barrack Obama in English
<https://github.com/NLP2RDF/DBpediaOpenDBpediaTextExtractionChallenge/blob/master/BO.ttl>
). Challenge participants are asked to wrap their NLP and extraction engines
in Docker images and submit them to us. We will run participants’ tools in
regular intervals in order to extract:
1. Facts, relations, events, terminology, ontologies as RDF triples
(Triple track)
2. Useful NLP annotations such as pos-tags, dependencies, co-reference
(Annotation track)
We allow submissions 2 months prior to selected conferences (currently
http://ldk2017.org/ and http://2017.semantics.cc/ ). Participants that fulfil
the technical requirements and provide a sufficient description will be able to
present at the conference and be included in the yearly proceedings. Each
conference, the challenge committee will select a winner among challenge
participants, which will receive 1000€.
Results
Every December, we will publish a summary article and proceedings of
participants’ submissions at http://ceur-ws.org/ . The first proceedings are
planned to be published in Dec 2017. We will try to briefly summarize any
intermediate progress online in this section.
Acknowledgements
We would like to thank the Computer Center of Leipzig University to give us
access to their 6TB RAM server Sirius to run all extraction tools.
The project was created with the support of the H2020 EU project HOBBIT
<https://project-hobbit.eu/> (GA-688227) and ALIGNED
<http://aligned-project.eu/> (GA-644055) as well as the BMWi project Smart
Data Web <http://smartdataweb.de/> (GA-01MD15010B).
Challenge Committee
· Sebastian Hellmann, AKSW, DBpedia Association, KILT Competence
Center, InfAI, Leipzig
· Sören Auer, Fraunhofer IAIS, University of Bonn
· Ricardo Usbeck, AKSW, Simba Competence Center, Leipzig University
· Dimitris Kontokostas, AKSW, DBpedia Association, KILT Competence
Center, InfAI, Leipzig
· Sandro Coelho, AKSW, DBpedia Association, KILT Competence Center,
InfAI, Leipzig
Contact Email: [email protected]
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