<html><head><meta http-equiv="Content-Type" content="text/html; charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Future Generation Computer Systems, SCIE-indexed</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Impact Factor (5.768)</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Special Issue on Machine Learning and Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""> Important Dates</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span class="Apple-tab-span" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); white-space: pre;"> </span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Paper submission due: 30 November, 2019</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Initial review feedback: 31 January, 2020</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Revision Due: 31 March, 2020</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Final review decision: 31 July, 2020</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">For more details: </span><a href="https://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/special-issue-on-machine-learning-and-knowledge-graphs" class="">https://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/special-issue-on-machine-learning-and-knowledge-graphs</a><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""> Overview:</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span class="Apple-tab-span" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); white-space: pre;"> </span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Machine learning and knowledge graphs are currently essential technologies for designing and building large scale distributed intelligent systems. Machine learning is a well established field, which has currently gained a high momentum due to the advances in the computational infrastructures, availability of Big Data, and appearance of new techniques such as deep learning. In fact, Deep Learning methods have become an important area of research, achieving some important breakthrough in various research fields, especially Natural Language Processing (NLP), Image and Speech Recognition.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Knowledge Graphs are large networks of real-world entities described in terms of their semantic types and their relationships to each other. Knowledge graphs is a recent technology with very high practical impact: examples include the Google Knowledge Graph with over 70 billion facts (in 2016), dataCommons, DBPedia, YAGO and YAGO2, Wikidata and Knowledge Vault, a very large scale probabilistic knowledge graph created with information extraction methods for unstructured or semi-structured information. Specifically, Knowledge Graphs provide the means of development of the newest methods for data management, data fusion / data merging, and graph and network optimization and modeling, serving as a source of high quality data and a base for ubiquitous information integration. While Knowledge Graphs provide explicit knowledge representations in terms of underlying ontologies based on symbolic logic, machine learning, such as Deep Learning technologies, provide implicit or latent r!</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">epresenta</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">tions of the knowledge contained in their models.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Although machine learning (and particularly, recently deep learning) and Knowledge Graphs technologies have been deployed separately, in the last years, the first works combining these technologies are showing large potential in solving many real-world challenges.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">In order to pursue more advanced methodologies, it has become critical that the communities related to Machine Learning, Deep Learning and Knowledge Graphs join their forces in order to develop more effective algorithms and applications. In particular, two main technology directions solicited for this special issue are as follows:</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">i) Improved Machine Learning with Knowledge Graphs: employing semantic models and linked data for the training steps, learning effective representation from the Knowledge Graphs for the tasks of feature extraction, classification, prediction and decision making. A successful example here includes IBM Watson question answering system, that has outperformed the best human players of an intellectual TV quiz show "jeopardy!"</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">ii) Machine Learning for Improving Knowledge Graphs: while capturing semantics correctly is impossible without at least some human involvement, machine learning can assist the knowledge acquisition of the semantic structures substantially. For example, knowledge graphs can be created by employing Deep learning, and then subsequently verified by the humans.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">A feature that explains the learnt knowledge graphs to the human needs to be inbuilt in the learning and verification process, making the resulting knowledge graph design solutions explainable to humans. Thus, the knowledge graphs created in this manner may be more efficient and provide more insights than the ones created solely by humans.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">The solicited contributions may comprise an overview of the outlined fields, and new technical solutions applicable to such use cases as manufacturing / smart factory, production 4.0 and its control of quality, mobility, smart cities, smart homes and buildings, energy efficiency, health and wellbeing, life sciences, libraries and archives, art.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Such use cases require both the analysis of data (e.g. sensor readings, distributed data processing) - i.e. quantitative information, and as well as qualitative (e.g. information semantically defining the production quality criteria and what is considered to be the deviation from them). As physical infrastructures and architectures (stemming from the fields of Grid and Cloud Computing, Big Data and Internet of Things), have a very high impact on the solutions to be suggested, we consider that Future Generation Computer Systems is the perfect venue for publishing this special issue, given the research area of the journal.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""> Topics of Interests:</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">We invite submission of high-quality manuscripts reporting relevant research in the area of generation of knowledge graphs by using deep learning techniques. Topics of interest include, but are not limited to:</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Architectures for systems based on Machine Learning and Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Machine Learning and Knowledge Graphs in distributed large scale systems</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Information management with on Machine Learning and Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Probabilistic Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Creation and maintenance (curation, quality assurance...) of Knowledge Graphs employing Machine Learning</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Machine Learning techniques employing Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Explainable Artificial Intelligence for data-intensive systems based on Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Non-functional application of Knowledge Graphs in data-intensive systems e.g. for legal use of data, user consent solicitation, smart contracting, GDPR</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- New approaches for combining Deep Learning and Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Methods for generating Knowledge Graph (node) embeddings</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Scalability issues</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Temporal Knowledge Graph Embeddings</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Applications of combining Deep Learning and Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Recommender Systems leveraging Knowledge Graphs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Link Prediction and completing KGs</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Ontology Learning and Matching exploiting Knowledge Graph-Based Embeddings</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Knowledge Graph-Based Sentiment Analysis</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Natural Language Understanding/Machine Reading</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Question Answering exploiting Knowledge Graphs and Deep Learning</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Entity Linking</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Trend Prediction based on Knowledge Graphs Embeddings</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Domain Specific Knowledge Graphs (e.g. Smart Cities, Scholarly, Biomedical, Musical)</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Applying knowledge graph embeddings to real world scenarios.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""> Submission Instructions:</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Manuscripts must be submitted through the electronic submission system (</span><a href="https://www.evise.com/evise/faces/pages/oversight/Oversight.jspx" class="">https://www.evise.com/evise/faces/pages/oversight/Oversight.jspx</a><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">), and please choose "VSI: ML & KGraphs".</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">Papers will be evaluated for their originality, contribution significance, soundness, clarity, and overall quality. The interest of contributions will be assessed in terms of technical and scientific findings, contribution to the knowledge and understanding of the problem, methodological advancements, and/or applicative value.</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""> Guest Editors:</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">********************************</span><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Mehwish Alam, FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, </span><a href="mailto:mehwish.alam@fiz-karlsruhe.de" class="">mehwish.alam@fiz-karlsruhe.de</a><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Anna Fensel, University of Innsbruck, </span><a href="mailto:anna.fensel@sti2.at" class="">anna.fensel@sti2.at</a><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Jorge Martinez-Gil, Software Competence Center Hagenberg GmbH, </span><a href="mailto:jorge.martinez-gil@scch.at" class="">jorge.martinez-gil@scch.at</a><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Bernhard A. Moser, Software Competence Center Hagenberg GmbH, </span><a href="mailto:bernhard.moser@scch.at" class="">bernhard.moser@scch.at</a><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Diego Reforgiato Recupero, University of Cagliari, </span><a href="mailto:diego.reforgiato@unica.it" class="">diego.reforgiato@unica.it</a><br style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Harald Sack, FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, </span><a href="mailto:harald.sack@fiz-karlsruhe.de" class="">harald.sack@fiz-karlsruhe.de</a></body></html>