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{{Paper
|id=Vol-2477/preface
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|pdfUrl=https://ceur-ws.org/Vol-2477/preface.pdf
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<pdf width="1500px">https://ceur-ws.org/Vol-2477/preface.pdf</pdf>
<pre>
Preface1

The life sciences domain has been an early adopter of linked data and, a consider-
able portion of the Linked Open Data cloud is composed of life sciences data sets.
The deluge of in flowing biomedical data, partially driven by high-throughput
gene sequencing technologies, is a key contributor and motor to these devel-
opments. The available data sets require integration according to international
standards, large-scale distributed infrastructures, specific techniques for data
access, and offer data analytics benefits for decision support. Especially in com-
bination with Semantic Web and Linked Data technologies, these promises to
enable the processing of large as well as semantically heterogeneous data sources
and the capturing of new knowledge from those.
    This workshop sought papers dealing with life sciences and biomedical data
processing, as well as with the amalgamation with Linked Data and Seman-
tic Web technologies for better data analytics, knowledge discovery and user-
targeted applications. The main goal was to provide useful information for the
Knowledge Acquisition research community as well as the working Data Scien-
tist.
    The specific focus of the workshop were theoretical and practical methods
and techniques that present the anatomy of large scale linked data infrastructure.
This covers: the distributed infrastructure to consume, store and query large
volumes of heterogeneous linked data; using indexes and graph aggregation to
better understand large linked data graphs, query federation to mix internal and
external data-sources, and linked data visualisation tools for health care and life
sciences. It will further cover topics around data integration, data profiling, data
curation, querying, knowledge discovery, ontology mapping / matching / recon-
ciliation and data / ontology visualisation, applications / tools / technologies /
techniques for life sciences and biomedical domain. SeWeBMeDA aims to pro-
vide researchers in biomedical and life science, an insight and awareness about
large scale data technologies for linked data, which are becoming increasingly
important for knowledge discovery in the life sciences domain.
    In this context, we were happy to see the accepted papers cover three rather
varied thematic areas fitting the workshop overall goals: 1) Ontology Matching
and Alignment 2) Life Science Linked Open Data (LS-LOD) 3) Semantic aware
Clinical Practices and Decision Support Enjoy!

October 2019
                                                                    Ali Hasnain
                                                                    Vı́t Nováček
                                                               Michel Dumontier
                                            Dietrich Rebholz-Schuhmann (chairs)


1
    Copyright ©2019 for this text by its authors. Use permitted under Creative
    Commons License Attribution 4.0 International (CC BY 4.0).

</pre>