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Semantics in the Deep: Semantic Analytics for Big Data - Workshop at the 14th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2018)
2018-05-25 to 2018-05-27
Rhodes, Greece
Recent advances in availability of information on the Internet, storage space and web generated content have paved the way for the advent of Big Data. The well-known 4 Vs (Velocity, Variety, Volume, Value) that characterize Big Data can find a match in intelligent ways for management, manipulation and value-extraction. It is widely acknowledged that the recent surge in AI and especially machine learning is exactly due to these advancements. The Semantic Web can offer a well-studied, although ever advancing, toolbox that can address Big Data requirements and contribute towards their meaningful analysis. Still, there are often issues that need to be tackled with like bootstrapping, efficiency and standardized business processes for semantic analytics to achieve satisfactory results. To this end, machine- and deep-learning techniques, while being shunned in the past, have been shown to have considerable contributions towards Big Data analytics and to overcome Semantic Web inherent limitations.
Therefore, the aim of Semantics in the Deep: Semantic Analytics for Big Data workshop is to bring together researchers and practitioners to look deeper into how Semantic Web technologies can contribute towards Big Data analytics. This can be achieved either by extracting value out of these data (through reasoning), creating sustainable ontology models, offering a solid foundation for deploying learning techniques or anything in between. link

2nd Workshop on Curative Power of MEdical DAta (MEDA 2018)
Fort Worth, Texas, USA

Organizing Committee:
Diana Trandabăț ("Alexandru Ioan Cuza" University of Iași, Romania)
Daniela Gîfu ("Alexandru Ioan Cuza" University of Iași, Romania Romanian Academy - Iaşi branch)
Kevin Cohen (Computational Bioscience Program U. Colorado School of Medicine)
Jingbo Xia (Huazhong Agricultural University, P.R. China)

In an era when massive amounts of medical data became available, researchers working in biological, biomedical and clinical domains have increasingly started to require the help of language engineers to process large quantities of biomedical and molecular biology literature (such as PubMed), patient data or health records. Linking the contents of these documents to each other, as well as to specialized ontologies, could enable access to and discovery of structured clinical information and foster a major leap in natural language processing and health research.
MEDA-2018 aims to gather innovative approaches for the exploitation of biomedical data using semantic web technologies and linked data by bringing together practitioners, researchers, and scholars to share examples, use cases, theories and analysis of biomedical data. The main objective of this second edition workshop is to consolidate an internationally appreciated forum for scientific research in BioMed, with emphasis on crowdsourcing, semantic web, knowledge integration and data linking.

The scientific program of MEDA-2018 will focus around the following topics:

Crowdsourcing approaches in biomedicine
Collaborative computational technologies for biomedical research
Biomedical digital libraries
Mining biomedical literature
Event-based text mining for biology and related fields
Event and entity extraction in medical texts
Conceptual graphs extracted from medical texts
Annotation of semantic content, with applications in medicine and biology
Techniques for Big Data in Healthcare
Medical search engines
Distributed communication system in biomedical applications
Deep learning for bioinformatics
Biomedical question/answering
Biomedical topic modeling
Biomedical language systems
Text summarization in the biomedical domain. link