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.
Topics
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
Dates
Submission Deadline December 19th, 2014
Notification of acceptance/rejection February 27th, 2015
Camera-Ready Paper Deadline April 17st, 2015
Submission https://easychair.org/conferences/?conf=jbmsbioir2015
Template http://www.biomedcentral.com/download/tex/bmc_article.zip
Length of paper 20 to 30 pages