Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision
Previous revision
sedami:start [2021/08/11 21:24] – schedule gjnsedami:start [2023/09/29 06:57] (current) – [Schedule] sbk
Line 6: Line 6:
 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. 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.
  
-**Organising committee**+The 2nd edition of SEDAMI will be co-located with the [[https://ecai2023.eu/|26th European Conference on Artificial Intelligence (ECAI 2023)]].
  
-  * Martin Atzmueller,  Osnabrück University, Germany, +The [[start2021|1st edition of SEDAMI]] was co-located with [[https://ijcai-21.org/|30th International Joint Conference on Artificial Intelligence (IJCAI-21)]] see [[https://ceur-ws.org/Vol-3032/|CEUR-WS Vol-3032]].
-  * Grzegorz J. Nalepa,  Jagiellonian University, Poland +
-  * Szymon Bobek,  Jagiellonian University +
-  * Nada Lavrac, Jožef Stefan Institute, Slovenia +
-====== SEDAMI 2021 at IJCAI 2021 ======+
  
-===== Important dates===== + 
-  * ** Submission Deadline:** May 132021 (AoE) +====== SEDAMI 2023 at ECAI 2023 ====== 
-  * //We plan to have a rolling review process for late/breaking papers of 5-6 ppThis will be kept open for up to 1 months after the regular deadlines/+ 
-  * ** Notification of Acceptance:** May 252021 (AoE) +====== Program ====== 
-  * ** Camera-Ready Versions Due:** June 6, 2021 (AoE) +  * The workshop will take place on SundayOctober 1st  
-  * ** Workshop date:** August 21-262021+  * Location: [[https://maps.app.goo.gl/A4VV2HTryfdaa5E36|WMI]],  Room[[https://intra.matinf.uj.edu.pl/plan/| 0086]] 
 +  * Session starts at 9:00see [[https://ecai2023.eu/acceptedworkshops|ECAI Program]]
  
 ===== Schedule ===== ===== Schedule =====
 +The presentation should be around 15 minutes with 5 minutes for questions.
 +The total time reserved from one presentation is 20 minutes.
  
-**Aug 20 10:00 – 13:30 Montreal Time (UTC-4)**+^Time^Accepted paper^ 
 +| 09:00 - 09:10 | Introduction to the workshop | 
 +| 09:10 - 09:30 | Visual patterns in an interactive app for analysis based on control charts and SHAP values \\ //Iwona Grabska-Gradzińska, Maciej Szelążek, Szymon Bobek and Grzegorz J. Nalepa // | 
 +| 09:30 - 09:50 | Improving understandability of explanations with a usage of expert knowledge \\ //Maciej Szelążek, Szymon Bobek and Grzegorz J. Nalepa// | 
 +| 09:50 - 10:20 | Post–Mining on Association Rule Bases \\ //Dietmar Seipel, Marcel Waleska, Daniel Weidner, Sven Rausch and Martin Atzmueller//
 +| 10:20 - 10:30 | Leveraging Graph Embedding for Opinion Leader Detection in Dynamic Social Networks \\ //Yunming Hui, Melisachew Wudage Chekol and Shihan Wang// |
  
-Please note, that all times are in UTC-4 (this is e.g., CEST-6 ... 10:00 UTC-4 is 16:00 CEST) 
  
-**10:00-10:15** SEDAMI 2021 - Opening (Chair: Martin Atzmueller) 
  
-**10:15-11:45** Session 1 - Foundations (Chair: Szymon Bobek)\\ +**Organising committee**
-10:15-10:45 Victor Guimarães and Vítor Costa: Meta-Interpretive Learning meets Neural Networks\\ +
-10:45-11:15 Blaž Škrlj and Nada Lavrač: Towards Explainable Relational Boosting via Propositionalization\\ +
-11:15-11:45 Dietmar Seipel and Martin Atzmueller: Declarative Knowledge Discovery in Databases via Meta-Learning - Towards Advanced Analytics+
  
-**11:45-12:00** Break+  Szymon Bobek,  Jagiellonian University 
 +  Martin Atzmueller,  Osnabrück University & German Research Center for AI (DFKI), Germany, 
 +  Nada Lavrac, Jožef Stefan Institute, Slovenia
  
-**12:00-13:00** Session 2 - Modeling & Application (Chair: Nada Lavrac)\\ +===== Important dates===== 
-12:00-12:30 Shaobo Wang, Guangliang Liu, Wenyan Zhu, Zengtao Jiao, Haichen Lv, Jun Yan and Yunlong Xia: Interpretable Knowledge Mining for Heart Failure Prognosis Risk Evaluation\\ +  * **Submission Deadline:** 31.07.2023 
-12:30-13:00 Dan Hudson, Leonid Schwenke, Stefan Bloemheuvel, Arnab Ghosh Chowdhury, Nils Schut and Martin Atzmueller: Knowledge-Augmented Induction of Complex Networks on Supply-Demand-Material Data+  * **Notification of Acceptance:** <del>14.08.2023</del>16.08.2023 
 +  * **Camera-Ready Versions Due:** <del>21.08.2023</del>11.09.2023 
 +  * **Workshop date:** 30 September 1 October 2023
  
-**13:00-13:30** Closing (Chair: Grzegorz J. Nalepa) 
  
 ===== Call for papers ===== ===== Call for papers =====
  
-{{ :sedami:sedami2021-cfp.pdf |Call For Papers -- SEDAMI 2021}} +  * Full CFP: {{ :sedami:sedami2023-cfp.pdf |Call for papers}} 
-===== Motivation for the workshop =====+  * One-pager: {{ :sedami:sedami2023-cfp-one-pager.pdf | Call for papers}} 
 +===== Aims and Scope =====
 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. 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. 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. 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.
  
 ===== Topics of interest ===== ===== Topics of interest =====
Line 55: Line 62:
 topics which include but are not limited to: topics which include but are not limited to:
  
-  * Declarative data mining 
-  * Declarative domain knowledge 
-  * Knowledge modelling and data mining 
-  * Data mining and machine learning using ontologies 
   * Introduction of semantics into the data mining process   * Introduction of semantics into the data mining process
-  * Interpretable models in data mining and machine learning +  * Explainable artificial intelligence and domain knowledge 
-  * Knowledge-based data mining and machine learning approaches +  * Declarative domain knowledge 
-  * Role of explanations in data mining and machine learning +  * Declarative data mining 
-  * Knowledge-graphs in data mining and machine learning+  * 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   * Feature engineering for transparency and explanation
-  * Transparent and hybrid models in machine learning+  * 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   * Inductive logic programming and data mining
 +  * Transparent and hybrid models in data mining
   * Human in the loop of the data mining process   * Human in the loop of the data mining process
   * Role of Linked Open Data in data mining   * Role of Linked Open Data in data mining
   * Applications of all of the above   * Applications of all of the above
- 
- 
  
  
 ===== Program Committee (tentative) ===== ===== Program Committee (tentative) =====
 +  * Sören Auer, Leibniz University of Hannover & TIB, Germany
   * Klaus-Dieter Althoff, University of Hildesheim & DFKI, Germany   * Klaus-Dieter Althoff, University of Hildesheim & DFKI, Germany
-  * Martin Atzmueller, Osnabrück University, Germany 
   * Przemysław Biecek, Warsaw University of Technology, Poland   * Przemysław Biecek, Warsaw University of Technology, Poland
-  * Szymon BobekJagiellonian University, Poland +  * Johannes FürnkranzJohannes Kepler University LinzAustria 
-  * João Gama, University of Porto, Portugal   +  * João Gama, University of Porto, Portugal 
-  * Nada LavracJožef Stefan InstituteSlovenia+  * Kristian KerstingTU DarmstadtGermany
   * Stan Matwin, Dalhousie University, Canada   * Stan Matwin, Dalhousie University, Canada
-  * Grzegorz J. Nalepa, Jagiellonian University, Poland 
   * Sławomir Nowaczyk, Halmstad University, Sweden   * Sławomir Nowaczyk, Halmstad University, Sweden
   * Jose Palma, Universidad de Murcia, Spain   * Jose Palma, Universidad de Murcia, Spain
-  * Juan PavonUniversidad Complutense de MadridSpain+  * Mykola PechenizkyiTU EindhovenThe Netherlands
   * Marc Plantevit, Université Lyon, France   * Marc Plantevit, Université Lyon, France
   * Eric Postma, Tilburg University, The Netherlands   * Eric Postma, Tilburg University, The Netherlands
   * Céline Rouveirol, Université Sorbonne Paris Nord, France   * Céline Rouveirol, Université Sorbonne Paris Nord, France
 +  * Ute Schmid, University of Bamberg, Germany
   * Marek Sikora, Silesian University of Technology, Poland   * Marek Sikora, Silesian University of Technology, Poland
 +  * Dietmar Seipel, University of Würzburg, Germany
   * Blaž Škrlj, Jožef Stefan Institute, Slovenia   * 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
  
  
Line 98: Line 111:
 ===== Submission details ===== ===== Submission details =====
  
-Please submit papers using the dedicated [[https://easychair.org/conferences/?conf=sedami2021|Easychair]]  We are accepting short papers – 5-6 pages with references, and long papers – 10-12 pages. We are encouraging both original research papers, as well position papers. All submissions should be formatted using the [[https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines|Springer LNCS format]].  Workshop proceedings will be made available by CEUR-WS. A post workshop journal publication is considered. 
  
-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. Should IJCAI 2021 be held online because of the COVID-19 situation, then we are willing to hold the workshop online.+{{:sedami:ccis-logo.jpg?200 |}}The publication of proceedings (full papers only) of the SEDAMI will  be part of the [[https://www.springer.com/series/7899|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 [[https://www.springer.com/gp/computer-science/lncs/open-access-publishing-in-computer-proceedings|here]]. 
 + 
 +Please submit papers using the dedicated [[https://easychair.org/conferences/?conf=sedami2023|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 [[https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines|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: All submitted papers must:
Line 107: Line 123:
   * be formatted according to the [[https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines|Springer LNCS template]];   * be formatted according to the [[https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines|Springer LNCS template]];
   * be in PDF (make sure that the PDF can be viewed on any platform).   * be in PDF (make sure that the PDF can be viewed on any platform).
 +
  
sedami/start.1628717057.txt.gz · Last modified: 2021/08/11 21:24 by gjn
Driven by DokuWiki Recent changes RSS feed Valid CSS Valid XHTML 1.0