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.

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
• Declarative domain knowledge
• Declarative data mining
• Knowledge modelling 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

We are aiming at the 0.5-day workshop that includes regular paper presentations of peer reviewed research papers (4-6 pages), as well as peer-reviewed short or position papers (2-4 pages). Furthemore, 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.