I became interested in these statistics myself, sometime ago. Eventually
tracked down a fairly interesting paper on the subject. As per quality vs.
quality that may be a different discussion. Here is a source from which
such statistics may have been obtained.
*Bo-Christer Björk, Annikki Roos, Mari Lauri:*
*Global annual volume of peer reviewed scholarly articles and the share
available via different Open Access options
http://elpub.scix.net/data/works/att/178_elpub2008.content.pdf.*
ELPUB 2008: 178-186
On Fri, Sep 19, 2014 at 3:34 AM, Alexander Garcia Castro
alexgarc...@gmail.com wrote:
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
ngo...@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
--
- cheers
Delroy Cameron http://knoesis.org/researchers/delroy/
*LinkedIn https://www.linkedin.com
