=Paper=
{{Paper
|id=Vol-3032/preface
|storemode=property
|title=Semantic Data Mining: A Brief Outline
|pdfUrl=https://ceur-ws.org/Vol-3032/SEDAMI2021-Paper1-MartinAtzmueller.pdf
|volume=Vol-3032
|authors=Martin Atzmueller,Grzegorz J. Nalepa,Szymon Bobek,Nada Lavrac
}}
==Semantic Data Mining: A Brief Outline
==
https://ceur-ws.org/Vol-3032/SEDAMI2021-Paper1-MartinAtzmueller.pdf
Semantic Data Mining: A Brief Outline
Martin Atzmueller1[0000−0002−2480−6901] ,
Grzegorz J. Nalepa2[0000−0002−8182−4225] ,
Szymon Bobek2[0000−0002−6350−8405] , and
Nada Lavrac3[0000−0002−9995−7093]
1
Semantic Information Systems Group, Osnabrück University, Germany
martin.atzmueller@uni-osnabrueck.de
2
GEIST Research Group, Jagiellonian University, Poland
gjn@gjn.re, szymon.bobek@uj.edu.pl
3
Jožef Stefan Institute, Ljubljana, Slovenia
nada.lavrac@ijs.si
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 min-
ing/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 declara-
tive 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 Guimarães and Vı́tor Costa discusses a structure
learning system based on meta-interpretive learning. The paper Towards Explainable
Relational Boosting via Propositionalization by Blaž Škrlj and Nada Lavrač 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 orga-
nizers 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.
Osnabrück, Ljubljana, Krakow – August 2021
Martin Atzmueller, Grzegorz J. Nalepa, Szymon Bobek, Nada Lavrac
References
1. Atzmueller, M.: Declarative Aspects in Explicative Data Mining for Computational Sensemak-
ing. In: Seipel, D., Hanus, M., Abreu, S. (eds.) Proc. International Conference on Declarative
Programming. pp. 97–114. Springer, Heidelberg, Germany (2018)
2. Atzmueller, M., Lemmerich, F., Reutelshoefer, J., Puppe, F.: Wiki–enabled semantic data
mining–task design, evaluation and refinement. In: Proc. International Workshop on Design,
Evaluation and Refinement of Intelligent Systems (DERIS), vol. CEUR–WS. vol. 545 (2009)
3. Atzmueller, M., Puppe, F., Buscher, H.P.: Exploiting Background Knowledge for Knowledge-
Intensive Subgroup Discovery. In: Proceedings of the 19th International Joint Conference on
Artificial Intelligence (IJCAI-05). pp. 647–652. Edinburgh, Scotland (2005)
4. Atzmueller, M., Seipel, D.: Declarative specification of ontological domain knowledge for de-
scriptive data mining (extended version). In: Proceedings of the 18th International Conference
on Applications of Declarative Programming and Knowledge Management. Spriner (2008)
5. Atzmueller, M., Sternberg, E.: Mixed-initiative feature engineering using knowledge graphs.
In: Proceedings of the 9th International Conference on Knowledge Capture (K-Cap). ACM
Press, New York, NY, USA (2017)
6. Bobek, S., Nalepa, G.J., Ślażyński, M.: Heartdroid—rule engine for mobile and context-aware
expert systems. Expert Systems 36(1), e12328 (2019)
7. Cespivova, H., Rauch, J., Svatek, V., Kejkula, M.: Roles of medical ontology in association
mining crisp-dm cycle. In: Proceedings of the ECML/PKDD 2004 Workshop on Knowledge
Discovery and Ontologies. Pisa, Italy (2004)
8. Che, D., Safran, M., Peng, Z.: From big data to big data mining: challenges, issues, and
opportunities. In: International conference on database systems for advanced applications. pp.
1–15. Springer (2013)
9. Dou, D., Wang, H., Liu, H.: Semantic data mining: A survey of ontology-based approaches.
In: Proceedings of the 2015 IEEE 9th international conference on semantic computing (IEEE
ICSC 2015). pp. 244–251. IEEE (2015)
10. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery:
An Overview. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.)
Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI Press (1996)
11. Guven, C., Seipel, D., Atzmueller, M.: Applying ASP for Knowledge-Based Link Prediction
with Explanation Generation in Feature Rich Networks. IEEE Transactions on Network
Science and Engineering 8(2) (April–June 2021)
12. Kralj, J., Robnik-Sikonja, M., Lavrac, N.: Netsdm: Semantic data mining with network
analysis. Journal of Machine Learning Research 20(32), 1–50 (2019)
13. Kuo, Y.T., Lonie, A., Sonenberg, L., Paizis, K.: Domain ontology driven data mining: A
medical case study. In: DDDM ’07: Proceedings of the 2007 International Workshop on
Domain Driven Data Mining. pp. 11–17. ACM, New York, NY, USA (2007)
14. Kutt, K., Drazyk, D., Bobek, S., Nalepa, G.J.: Personality-based affective adaptation methods
for intelligent systems. Sensors 21(1), 163 (2021)
15. Lavrač, N., Škrlj, B., Robnik-Šikonja, M.: Propositionalization and embeddings: two sides of
the same coin. Machine Learning 109(7), 1465–1507 (2020)
16. Lavrač, N., Vavpetič, A.: Relational and semantic data mining. In: International Conference
on Logic Programming and Nonmonotonic Reasoning. pp. 20–31. Springer (2015)
17. Lavrač, N., Vavpetič, A., Soldatova, L., Trajkovski, I., Novak, P.K.: Using ontologies in
semantic data mining with segs and g-segs. In: International Conference on Discovery Science.
pp. 165–178. Springer (2011)
18. Ławrynowicz, A.: Semantic Data Mining - An Ontology-Based Approach, Studies on the
Semantic Web, vol. 29. IOS Press (2017)
19. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521(7553), 436–444 (2015)
20. Liu, H.: Towards semantic data mining. In: 9th International Semantic Web Conference
(ISWC2010). pp. 7–11 (2010)
21. Molnar, C., Casalicchio, G., Bischl, B.: Interpretable machine learning–a brief history, state-of-
the-art and challenges. In: Joint European Conference on Machine Learning and Knowledge
Discovery in Databases. pp. 417–431. Springer (2020)
22. Nalepa, G.J.: Modeling with Rules Using Semantic Knowledge Engineering, Intelligent
Systems Reference Library, vol. 130. Springer (2018)
23. Nalepa, G.J., Bobek, S., Kutt, K., Atzmueller, M.: Semantic data mining in ubiquitous sensing:
A survey. Sensors 21(13), 4322 (2021)
24. Nalepa, G.J., Kutt, K., Bobek, S.: Mobile platform for affective context-aware systems. Future
Generation Computer Systems 92, 490–503 (2019)
25. Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: A compre-
hensive survey. Web Semantics 36, 1–22 (2016)
26. Roscher, R., Bohn, B., Duarte, M.F., Garcke, J.: Explainable machine learning for scientific
insights and discoveries. Ieee Access 8, 42200–42216 (2020)
27. von Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., Kirsch, B.,
Pfrommer, J., Pick, A., Ramamurthy, R., Walczak, M., Garcke, J., Bauckhage, C., Schuecker,
J.: Informed machine learning – a taxonomy and survey of integrating knowledge into learning
systems (2020)
28. Schwenke, L., Atzmueller, M.: Show Me What You’re Looking For: Visualizing Abstracted
Transformer Attention for Enhancing Their Local Interpretability on Time Series Data. In:
Proc. 34th International Florida Artificial Intelligence Research Society Conference (FLAIRS-
2021). FLAIRS, North Miami Beach, FL, USA (2021)
29. Sikora, M., Wróbel, Ł., Gudyś, A.: Guider: a guided separate-and-conquer rule learning in
classification, regression, and survival settings. Knowledge-Based Systems 173, 1–14 (2019)
30. Sirichanya, C., Kraisak, K.: Semantic data mining in the information age: A systematic review.
International Journal of Intelligent Systems (2021)
31. Svátek, V., Rauch, J., Ralbovský, M.: Ontology-enhanced association mining. In: Semantics,
Web and Mining. LNCS, vol. 4289, pp. 163–179 (2005)
32. Szpyrka, M., Brzychczy, E., Napieraj, A., Korski, J., Nalepa, G.J.: Conformance checking of
a longwall shearer operation based on low-level events. Energies 13(24), 6630 (2020)
33. Vavpetič, A., Lavrač, N.: Semantic data mining system g-SEGS. In: In proceedings of the
Workshop on Planning to Learn and Service-Oriented Knowledge Discovery (PlanSoKD-11),
ECML PKDD conference, Athens, Greece, September 5-9. pp. 17–29 (2011)
34. Vollert, S., Atzmueller, M., Theissler, A.: Interpretable Machine Learning: A Brief Survey
From the Predictive Maintenance Perspective. In: Proc. IEEE International Conference on
Emerging Technologies and Factory Automation (ETFA 2021). IEEE (2021)
35. Weidner, D., Atzmueller, M., Seipel, D.: Finding maximal non-redundant association rules in
tennis data. In: Hofstedt, P., Abreu, S., John, U., Kuchen, H., Seipel, D. (eds.) Declarative
Programming and Knowledge Management - Conference on Declarative Programming, DE-
CLARE 2019, Unifying INAP, WLP, and WFLP, Revised Selected Papers. Lecture Notes in
Computer Science, vol. 12057, pp. 59–78. Springer (2019)
SEDAMI 2021: International workshop on Semantic Data Mining,
held (online) at IJCAI 2021 on 20th of August 2021
Editors
– Martin Atzmueller, Osnabrück University, Germany
– Grzegorz J. Nalepa, Jagiellonian University, Poland
– Szymon Bobek, Jagiellonian University, Poland
– Nada Lavrač, Jožef Stefan Institute, Slovenia
Program Committee
– Klaus-Dieter Althoff, University of Hildesheim & DFKI, Germany
– Martin Atzmueller, Osnabrück University, Germany
– Przemysław Biecek, Warsaw University of Technology, Poland
– Szymon Bobek, Jagiellonian University, Poland
– João Gama, University of Porto, Portugal
– Nada Lavrač, Jožef Stefan Institute, Slovenia
– Stan Matwin, Dalhousie University, Canada
– Grzegorz J. Nalepa, Jagiellonian University, Poland
– Sławomir Nowaczyk, Halmstad University, Sweden
– Jose Palma, Universidad de Murcia, Spain
– Juan Pavon, Universidad Complutense de Madrid, Spain
– Marc Plantevit, Université Lyon, France
– Eric Postma, Tilburg University, The Netherlands
– Céline Rouveirol, Université Sorbonne Paris Nord, France
– Marek Sikora, Silesian University of Technology, Poland
– Blaž Škrlj, Jožef Stefan Institute, Slovenia