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MLKgraphs 2019 : 1st International Workshop on Machine Learning and Knowledge Graphs
MLKgraphs 2019 : 1st International Workshop on Machine Learning and Knowledge Graphs

MLKgraphs 2019 : 1st International Workshop on Machine Learning and Knowledge Graphs

Linz, Austria
Event Date: August 26, 2019 - August 26, 2019
Submission Deadline: March 31, 2019
Notification of Acceptance: May 21, 2019
Camera Ready Version Due: June 08, 2019




Call for Papers

Call for Papers
1st International Workshop on Machine Learning and Knowledge Graphs
(MLKgraphs 2019)
Co-located with DEXA 2019
www.dexa.org/MLKgraphs2019/
Linz Austria, 26-29 August 2019

Knowledge Graphs are becoming a key technology for large-scale information processing systems containing massive collections of interrelated facts. Specifically, Knowledge Graphs provide the means for development of the newest data methods for data management, data fusion, data merging, and graph optimization and modeling, serving as a source of high quality data and a base for web-scale information integration.

The 1st International Workshop on Machine Learning and Knowledge Graphs aims to be a meeting point for researchers and practitioners working on the latest advances in the intersection of machine learning technologies and knowledge graphs. Therefore, we welcome submissions of novel research that brings together the two topics of Machine Learning (ML) and Knowledge Graphs (KGs) either applying ML models for semantic data management structures (like KGs or ontologies), or by presenting newly assembled Knowledge Graphs that support the task of Machine Learning for certain application domains. Examples areas are Business Analytics, Customer Relationship Management, Fault Detection, Industry 4.0, or Social Networking.

Topics of Interest

Machine Learning (plus its applications such as for Chatbots, Robotics, Social Networks, Fault Detection, Predictive Maintenance, Life Sciences, Neurosciences …) applied on semantic data management structures
Knowledge Graphs and Ontologies
State-of-the-art Data Management solutions for Machine Learning applications
Artificial Intelligence
Deep Learning
Cognitive Computing
Question Answering Systems
Image Analysis
Text Analytics
Industry 4.0
Internet of Things
Smart Cities

Important Dates:

Submission of full papers: March 31, 2019
Notification of acceptance: May 21, 2019
Camera-ready copies due: June 08, 2019

Organizers:

Anna Fensel, University of Innsbruck, Austria
Jorge Martinez-Gil, Software Competence Center Hagenberg, Austria
Bernhard Moser, Software Competence Center Hagenberg, Austria

Program committee:
Alessandro Adamou, Insight Centre for Data Analytics, Ireland
Mohamed Ben Ellefi, Aix Marseille Université, France
Jose Manuel Chaves-Gonzalez, University of Extremadura, Spain
Ioana Ciuciu, Babes-Bolyai University, Romania
Lisa Ehrlinger, Johannes Kepler University Linz & SCCH, Austria
Isaac Lera, University of the Balearic Islands, Spain
Ismael Navas Delgado, University of Malaga, Spain
Mario Pichler, Software Competence Center Hagenberg, Austria
Marta Sabou, Technical University Vienna, Austria
Harald Sack, FIZ Karlsruhe & Karlsruhe Institute of Technology, Germany
Iztok Savnik, University of Primorska, Slovenia
François Scharffe, Columbia University, USA
Marina Tropmann-Frick, HAW Hamburg, Germany


Proceedings

Authors are invited to submit electronically original contributions in English. Submitted papers should not exceed 10 pages (for a full paper) and 5 pages (for a short paper). For formatting guidelines, please refer to http://www.dexa.org/formatting_guidelines. All accepted papers will be published by Springer in "Communications in Computer and Information Science. One of the authors of an accepted paper must register to DEXA conference and present the paper at the workshop. For paper registration and electronic submission, please refer to: https://confdriver.ifs.tuwien.ac.at/dexa2019

Special Issue
Authors of selected papers of the workshop will be invited to submit extended versions of their papers which can be published in a special issue after evaluation.



Credits and Sources

[1] MLKgraphs 2019 : 1st International Workshop on Machine Learning and Knowledge Graphs


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