Semantic Data Mining: A Brief Outline

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When considering knowledge discovery in databases, data mining, and associated machine learning and data analytic methods, the general goal of data mining is to uncover novel, interesting, and ultimately understandable patterns, relating to valuable, useful and implicit knowledge [ 10 ]. Considering the development of data mining in the last decades, it can be observed that not only the addressed data mining tasks were more restricted, but also the applied data mining workflows were simpler than today. Thus, recent advances of data mining and machine learning address new challenges in its practical use for data analysis. This relates to, for example, novel processing, mining and learning methods and approaches, as well as large-scale and complex data representations [ 1, 8, 11, 19 ], which also includes important aspects of interpretability [ 21, 34 ] and explainability [ 1, 26, 28 ].

Using semantic information such as domain/background knowledge in data mining is a promising emerging direction for addressing these problems [ 3, 22, 33 ], where the domain knowledge is typically represented in a knowledge repository, such as an ontology, or a knowledge base [ 9, 25, 27, 30 ]. The main aspect of semantic data mining [ 2, 9, 15–17, 20, 23, 24 ], 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. There has been growing interest in this issue, e.g., [ 3–5, 18, 22, 31 ], in various domains, e. g., in the medical domain [ 4, 7, 12, 13, 17, 29 ] but also in human behavior analysis and industrial applications [ 5, 6, 14, 32, 35 ].

In summary, the term semantic data mining can be interpreted rather broadly as being concerned with the integration of semantic/domain knowledge into the data mining/knowledge discovery process, where in the respective methods and approaches, “semantic information” or “declarative knowledge” is meaningfully integrated into the data mining process. For example, this can relate to ontologies or to other declarative and/or rule-based mechanisms and formalizations w.r.t. feature construction and engineering, the semantics of attributes, and different post-processing approaches etc.

Copyright © 2021 for this paper by its authors.

Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

SEDAMI Workshop 2021 – Contextualization and Preface

The goal of the SEDAMI 2021 workshop is to offer an interdisciplinary forum for researchers working in the fields of semantic data mining. With this workshop we thus 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. The workshop seeks for contributions on methods, techniques and applications that are both domain-specific but also transversal to different application domains. In particular, this includes 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.

Submissions and Sessions

This proceedings volume comprises the papers of the SEDAMI 2021 workshop. In total, we received 7 submissions, from which we were able to accept five submissions based on a rigorous reviewing process.

Based on the set of accepted papers, we set up two sessions. The first session discusses the foundations of semantic data mining. The work Meta-Interpretive Learning meets Neural Networks by Victor Guimara˜es and V´ıtor Costa discusses a structure learning system based on meta-interpretive learning. The paper Towards Explainable Relational Boosting via Propositionalization by Blazˇ Sˇkrlj and Nada Lavracˇ describes an approach improving black-box classifiers’ interpretability in a relational setting using propositionalization, also combining XGBoost with SHAP. In Declarative Knowledge Discovery in Databases via Meta-Learning - Towards Advanced Analytics, Dietmar Seipel and Martin Atzmueller propose a novel approach for declarative knowledge discovery in databases enabling advanced analytics via the concept of meta-learning.

The second session is concerned with modeling and application of semantic data mining. In Interpretable Knowledge Mining for Heart Failure Prognosis Risk Evaluation by Shaobo Wang, Guangliang Liu, Wenyan Zhu, Zengtao Jiao, Haichen Lv, Jun Yan and Yunlong Xia, a pipeline to mine interpretable knowledge from electronic health records in the context of Heart Failure (HF) prognosis risk evaluation is proposed. Finally, the paper Knowledge-Augmented Induction of Complex Networks on Supply-Demand-Material Data by Dan Hudson, Leonid Schwenke, Stefan Bloemheuvel, Arnab Ghosh Chowdhury, Nils Schut and Martin Atzmueller presents a method for matching items in a database according to their attributes, using knowledge of sub-contexts within the problem domain. The goal is to improve the specificity and relevance of matches, specifically within a challenging domain, i. e., supply chain modeling.

We thank all the participants of the workshop for their contributions and the organizers of the IJCAI 2021 conference for their support. Additionally, we want to thank the reviewers for their careful help in selecting and improving the accepted workshop papers.

We are looking forward to a very exciting and interesting workshop.

Osnabru¨ck, Ljubljana, Krakow – August 2021

Editors

Program Committee

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