SEDAMI webapge is http://sedami.geist.re

The theme of the SEDAMI workshop is semantic data mining. With this workshop we aim to get an insight into the current status of research in this area. We focus mainly on methods that allow include/utilize/exploit semantic information and domain knowledge in the context of machine learning and data mining, focusing on domains and research questions that have not been deeply investigated so far and to improve solutions to classic tasks. We encourage contributions on methods, techniques and applications that are both domain-specific but also transversal to different application domains. In particular, we solicit contributions that aim to focus on semantic data mining for providing and/or enhancing interpretability, the introduction and preservation of knowledge, as well as the provisioning of explanations.

The 2nd edition of SEDAMI will be co-located with the 26th European Conference on Artificial Intelligence (ECAI 2023).

The 1st edition of SEDAMI was co-located with 30th International Joint Conference on Artificial Intelligence (IJCAI-21) see CEUR-WS Vol-3032.

Organising committee

• Szymon Bobek, Jagiellonian University
• Martin Atzmueller, Osnabrück University & German Research Center for AI (DFKI), Germany,
• Nada Lavrac, Jožef Stefan Institute, Slovenia

• Submission Deadline: 31.07.2023
• Notification of Acceptance: 14.08.202316.08.2023
• Camera-Ready Versions Due: 21.08.202311.09.2023
• Workshop date: 30 September - 1 October 2023

• Full CFP: Call for papers
• One-pager: Call for papers

The general goal of data mining is to uncover novel, interesting, and ultimately understandable patterns, cf. (Fayyad 1996), i.e., relating to valuable, useful and implicit knowledge. Looking at the development of data mining in the last decades, it can be observed that not only the data mining tasks used to be more restricted, but also the applied data mining workflows were simpler. Thus, recent advances of data mining and machine learning apparently bring new challenges in its practical use in data mining, including interpretability, introduction and preservation of knowledge, as well as the provisioning of explanations. Using semantic information such as domain/background knowledge in data mining is a promising emerging direction for addressing these problems, where the knowledge is typically represented in a knowledge repository, such as an ontology, or a knowledge base. The main aspect of semantic data mining, which we focus on in this workshop, is the explicit integration of this knowledge into the data mining and knowledge discovery modeling step, where the algorithms for data mining/modeling or post-processing make use of the formalized knowledge to improve the overall results.

The aim of this workshop, is to get an insight into the current status of research in semantic data mining, showing how to include/utilize/exploit semantic information and domain knowledge in the context of machine learning and data mining, focussing on domains and research questions that have not been deeply investigated so far and to improve solutions to classic tasks.

We encourage contributions on methods, techniques and applications that are both domain-specific but also transversal to different application domains. In particular, we solicit contributions that aim to focus on semantic data mining for providing and/or enhancing interpretability, the introduction and preservation of knowledge, as well as the provisioning of explanations - thus addressing important principles, methods, tools and future research directions in this emerging field.

Overall, we are interested in receiving papers related to the following topics which include but are not limited to:

• Introduction of semantics into the data mining process
• Explainable artificial intelligence and domain knowledge
• Declarative domain knowledge
• Declarative data mining
• Declarative explainable artificial intelligence
• Integration of causal machine learning and expert knowledge
• Neuro-symbolic artificial intelligence
• Knowledge modeling and data mining
• Feature engineering for transparency and explanation
• Knowledge-based data mining approaches
• Knowledge-graphs in data mining
• Interpretable models in data mining
• Role of explanations in data mining
• Inductive logic programming and data mining
• Transparent and hybrid models in data mining
• Human in the loop of the data mining process
• Role of Linked Open Data in data mining
• Applications of all of the above

• Sören Auer, Leibniz University of Hannover & TIB, Germany
• Klaus-Dieter Althoff, University of Hildesheim & DFKI, Germany
• Przemysław Biecek, Warsaw University of Technology, Poland
• Johannes Fürnkranz, Johannes Kepler University Linz, Austria
• João Gama, University of Porto, Portugal
• Kristian Kersting, TU Darmstadt, Germany
• Stan Matwin, Dalhousie University, Canada
• Sławomir Nowaczyk, Halmstad University, Sweden
• Jose Palma, Universidad de Murcia, Spain
• Mykola Pechenizkyi, TU Eindhoven, The Netherlands
• Marc Plantevit, Université Lyon, France
• Eric Postma, Tilburg University, The Netherlands
• Céline Rouveirol, Université Sorbonne Paris Nord, France
• Ute Schmid, University of Bamberg, Germany
• Marek Sikora, Silesian University of Technology, Poland
• Dietmar Seipel, University of Würzburg, Germany
• Blaž Škrlj, Jožef Stefan Institute, Slovenia
• Jerzy Stefanowski, Poznan University of Technology, Poland
• Stefano Teso, KU Leuven, Belgium
• Gerhard Weikum, Max Planck Institute for Informatics, Germany
• Filip Železny, CTU, Prague, Czech Republic
• Agnieszka Ławrynowicz, Poznan University of Technology, Poznań, Poland
• Weronika T. Adrian, AGH UST, Krakow, Poland

The publication of proceedings (full papers only) of the SEDAMI will be part of the Springer's CCIS book series. It will be possible to make individual papers Open Access, at the discretion and cost of the authors, by following the Springer procedure described here.

Please submit papers using the dedicated Easychair We are accepting short papers that will not be published in Springer CCIS book – 5-6 pages (not including references), and full papers – 10-15 pages (not including references). We are encouraging both original research papers, as well position papers. All submissions should be formatted using the Springer LNCS format.

Furthermore, we encourage tool presentations. Depending on the number of submissions, long papers will be 20-30 minutes and short papers 15-20 minutes including Q&A. For the workshop we are expecting around 20-30 participants to attend.

All submitted papers must:

• be written in English;
• contain author names, affiliations, and email addresses;
• be formatted according to the Springer LNCS template;
• be in PDF (make sure that the PDF can be viewed on any platform).