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
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
-- Axel Ngonga, Dr. rer. nat Head of AKSW Augustusplatz 10 Room P905 04109 Leipzig http://aksw.org/AxelNgonga
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