Workshop Schedule
Accepted papers will be chosen for oral presentation and demos. In addition to presentations and demos for accepted work, we also plan to invite a leading researcher in Biomedical for a keynote speech. The tentative schedule of the workshop is as follow:
Sunday June 3, 2018.
- 14:00 - 14:10 Opening
- 14:10 - 14:40 Keynote by "Prof. Dr. Maria-Esther Vidal"
Maria-Esther Vidal (Universidad Simón Bolívar) is the head of the Scientific Data Management group at TIB Leibniz Information Centre for Science and Technology, Germany and a full professor (on-leave) at Universidad Simón Bolívar (USB) Venezuela. Her interests include Big data and knowledge management, knowledge representation, and semantic web. She has published more than 130 peer-reviewed papers in Semantic Web, Databases, Bioinformatics, and Artificial Intelligence. She has co-authored one monograph, and co-edited books and journal special issues. She is part of various editorial boards (e.g., JWS, JDIQ), and has been general chair, co-chair, senior member, and reviewer of several scientific events and journals (e.g., ESWC, AAAI, AMW, WWW, KDE). She is leading data management tasks in the EU H2020 projects iASiS and BigMedylitics and is part of the team of supervisors of WDAqua (MSCA-ETN project). Maria-Esther has been a visiting professor in different worldwide universities, e.g., University of Maryland College Park, University of Nantes, University Politecnica of Catulgna, and Karlsruhe Institute of Technology, and from 2016-2018, she has been a Senior Research Scientist at the Fraunhofer IAIS in Germany.
Title: Synthesizing Big Data into Actionable Knowledge
Big data plays an important role in promoting emerging scientific and interdisciplinary research by enabling decision-making. However, in order to provide actionable insights relevant for the support of decision-making, several challenges for data management, analytics, and knowledge discovery need to be addressed. In this talk, we will describe a knowledge-driven approach capable to ingest Big data sources and integrate them into a knowledge graph that represents not only the meaning of the entities published by these data sources, but also that provides the basis for the discovery of unknown patterns and associations between these entities. The features of this knowledge-driven framework are shown in the context of the EU funded project iASiS (http://project-iasis.eu/) , where it is used to pave the way for personalized diagnosis and treatments.
- 14:40 - 15:30
Using Schema Extraction for Query Design without Data Access to Enable Privacy Maintaining Processing of Sensitive Data .
(Lars C. Gleim, Md. Rezaul Karim, Lukas Zimmermann, Oliver Kohlbacher, Holger Stenzhorn, Stefan Decker and Oya Beyan)
Assessing FAIR data principles against the 5-star Open data principles.
(Ali Hasnain and Dietrich Rebholz-Schuhmann)
Quality Assessment of Biomedical Metadata Using Topic Modeling.
(Stuti Nayak, Amrapali Zaveri and Michel Dumontier
- 15:30 - 16:00 Coffee Break
- 16:00 - 17:45 Hackathon
Knowledge Graphs in the Biomedical domain are especially large in size and may capture data from many different topics. Integration of complementary graphs leads to an even more complex graph that captures more information and is thus more valuable. As part of the Personal Health Train (PHT) concept, we explore a privacy-preserving approach to predicting the value that integration. Being able to find suitable data sources without compromising their content allows to train machine learning algorithm on that source without compromising the data itself. As an effect, all benefits from using machine learning can be leveraged in domains where privacy plays a fundamental role. To make this possible, in this short Hachathon we will be working with Knowledge Graphs that capture Biomedical data, Information Extraction and Bloom Filters.
- 17:45 - 18:00 Closing