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2 articles every minute

From: Alexander Garcia Castro <alexgarciac@gmail.com>
Date: Fri, 19 Sep 2014 09:34:18 +0200
Message-ID: <CALAe=OLQG1+cGnL19m4szhYsbgwxGNBzw9zsRd-k0_BDuAQXag@mail.gmail.com>
To: Axel Ngonga <ngonga@informatik.uni-leipzig.de>
Cc: public-lod community <public-lod@w3.org>, "semantic-web@w3.org" <semantic-web@w3.org>, editor1@kdnuggets.com
Hi, I don't mean to be picky. I am just curious about statements like "2
articles every minute". Where do they come from? Where can I get this
stats? are this stats about journal papers? if this is true, I assume it
is, then shouldn't we start to consider that the quality of publications is
simply poor? Perhaps this is a challenge for us to clear the act instead of
a challenge for the technology; and if there is a challenge for the tech
then, IMHO, it should be how to remove rubbish from those 3000 articles per
day "Every day, approximately 3000 new bio-medical articles are published
on the Web".

Anyway, just woke up this morning and saw this "per day, 3000 new
bio-medical articles are published on the Web" and then "2 articles every
minute". Just in the biomedical domain and I thought, where does it come
from and what does it mean for us.





On Fri, Sep 19, 2014 at 8:03 AM, Axel Ngonga <
ngonga@informatik.uni-leipzig.de> wrote:

> Call for Papers
> ************
> Supplement on Semantics-Enabled Biomedical Information Retrieval
> Journal of Bio-Medical Semantics
>
> Important Data
> *************
> Submission Deadline: December 19th, 2014
> Notification of acceptance/rejection: February 27th, 2015
> Camera-Ready Paper Deadline: April 17th, 2015
> Webpage: http://bioasq.org/project/bioasq-special-issue
> Submission page: https://easychair.org/conferences/?conf=jbmsbioir2015
>
> Call
> ***
>
> Every day, approximately 3000 new bio-medical articles are published on
> the Web. This averages to more than 2 articles every minute. In addition to
> the sheer amount of bio-medical information available on the Web, the
> variety of this information increases everyday and ranges from structured
> data in the form of ontologies to unstructured data in the form of
> documents. Staying on top of this huge amount of diverse data requires
> methods that allow detecting and integrating portions of datasets that
> satisfy the information need of given users from sources such as documents,
> ontologies, Linked Data sets, etc. Developing tools to achieve this bold
> goal requires combining techniques from several disciplines including
> Natural Language Processing (e.g., question answering, document
> summarization, ontology verbalization), Information Retrieval (e.g.,
> document and passage retrieval), Machine Learning (e.g., large-scale
> hierarchical classification, clustering, etc.), Semantic Web/Linked Data
> (e.g., reasoning, link discovery) and Databases (e.g., storage and
> retrieval of triples, indexing, etc.).
>
> The aim of this supplement is to collect and present the newest results
> from these domains in order to push the research frontier towards
> information systems that will be able to deal with the whole diversity of
> the Web in the bio-medical domain.
>
> The topics of interest include (but are not restricted to):
>
> * Large-scale hierarchical text classification
> * Large-scale classification of documents onto ontology concepts (semantic
> indexing)
> * Classification of questions onto ontological concepts
> * Scalable approaches to document clustering
> * Text summarization, especially multi-document and query-focused
> summarization
> * Verbalization of structured information and related queries (RDF, OWL,
> SPARQL, etc.)
> * Question Answering over structured, semi-structured and unstructured data
> * Reasoning for information retrieval and question answering
> * Information retrieval over fragmented sources of information
> * Efficient indexing and storage structures for information retrieval
> * Delivery of the retrieved information in a concise and
> user-understandable form
> * Relation extraction
> * Textual entailment
> * Natural-language generation
> * Named entity recognition/disambiguation
> * Fact checking
> * Exploitation of semantic resources (terminologies, ontologies) for
> information retrieval and question answering
> * Normalisation of data resources with semantic resources, i.e.,
> concept-driven data transformation
>
> Cheers,
> Axel
>
> --
> Axel Ngonga, Dr. rer. nat
> Head of AKSW
> Augustusplatz 10
> Room P905
> 04109 Leipzig
> http://aksw.org/AxelNgonga
>
> Tel: +49 (0)341 9732341
> Fax: +49 (0)341 9732239
>
>
>


-- 
Alexander Garcia
http://www.alexandergarcia.name/
http://www.usefilm.com/photographer/75943.html
http://www.linkedin.com/in/alexgarciac
Received on Friday, 19 September 2014 07:35:07 UTC

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