=Paper=
{{Paper
|id=Vol-2576/xpreface
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|pdfUrl=https://ceur-ws.org/Vol-2576/xpreface.pdf
|volume=Vol-2576
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2576/xpreface.pdf</pdf>
<pre>
Preface

The journal track was intended as a forum for presenting significant Semantic
Web-related research results that have been recently published in well-known
and well-established journals but that have not been presented at a Semantic
Web-related conference. The goal was to highlight these results at ISWC
and promote discussions potentially leading to meaningful multi-disciplinary
collaborations.
    Traditionally only articles published in the Journal of Web Semantics
(JWS) and the Semantic Web Journal (SWJ) were considered for the ISWC
journal track. However, with the goal of enabling cross-fertilization with
other related communities, this year our two chairs, Claudia d’Amato and
Lalana Kagal, included additional journals such as: the Journal of Network
and Computer Applications, IEEE Transactions on Neural Networks and
Learning Systems, the Journal of Machine Learning Research, the Data Min-
ing and Knowledge Discovery Journal, ACM Transactions on the Web, ACM
Computing Surveys, IEEE Transactions on Knowledge and Data Engineer-
ing, ACM Transactions on Computer-Human Interaction, Artificial Intelli-
gence Journal, Proceedings of the Very Large Database Endowment and the
Journal of Information Science. Papers falling within the ISWC topics that
had been published within the listed journals starting from January 1st, 2017
were considered eligible for submission to the journal track.
    We received 24 extended abstract submissions, out of which 13 were ac-
cepted and collected as CEUR proceedings.
    Each submission was reviewed by at least two members of the 34 member
program committee in order to assess how interesting it is as well as its
novelty, relevance and attractiveness for the ISWC audience.
    Also taken into consideration was the quality of the extended abstracts
and the diversity of the topics, spanning from scalable reasoning and triple
storage, machine translation, fact predictions on (probabilistic) Knowledge
Graphs, modeling Linked Open data for different domains, and semantic
sensor networks.

   October, 2019
                                    Claudia d’Amato and Lalana Kagal

</pre>