=Paper=
{{Paper
|id=None
|storemode=property
|title=Planning to Learn: Recent Developments and Future Directions
|pdfUrl=https://ceur-ws.org/Vol-950/planlearn2012_abstract_Filip_Zelezny.pdf
|volume=Vol-950
}}
==Planning to Learn: Recent Developments and Future Directions==
https://ceur-ws.org/Vol-950/planlearn2012_abstract_Filip_Zelezny.pdf
Planning to learn: Recent Developments and
Future Directions
Filip Železný
Czech Technical University in Prague, Faculty of Electrical Engineering
Technická 2, 16627 Prague 6, Czech Republic
zelezny@fel.cvut.cz
The talk will cover my lab’s recent research concerning planning to learn and
discuss its relationships to relevant work of other researchers.
I will first introduce a machine-learning application that had motivated us
to explore how knowledge-discovery workflows could be designed automatically
using a data-mining ontology. In particular, we mined product-engineering data
such as CAD documents for structural design patterns [1]. This task entailed the
orchestration of numerous data-preprocessing and machine-learning algorithms
in surprisingly complex workflows. The involved technique of sorted refinement
[2] lead to non-linear, non-tree knowledge discovery workflows, in that the data
flow was forked into individually processed data streams later reuniting as inputs
to an inductive logic programming algorithm.
Given the circumstances above, we wanted to see if the user could be allevi-
ated from composing such complex workflows manually. To this end, we started
to develop a knowledge-discovery ontology to capture the functionalities, con-
straints and mutual relations among data mining algorithms. Building on estab-
lished strategies for automatic planning, we implemented a planning algorithm
that proposes a suitable workflow using knowledge represented in the ontology,
accessed by the planner through the standard SPARQL querying interface. I
will review the main concepts of the ontology and the planning task, following
mainly the paper [3].
Next I will present how the mentioned developed concepts were integrated
and exploited in the machine-learning suite Orange4WS in collaboration with
the Dept. of Knowledge Technologies at the Jozef Stefan Institute in Slovenia.
This part will be based mainly on the paper [4]. I will also review our latest
developments in an alternative approach to workflow planning [5], which ex-
ploits the definitions of some domain-specific experience-proven workflows. Such
established workflows are viewed as initial templates, which are algorithmically
optimized by exploring their variations such as a replacement of an algorithm by
a functionally similar algorithm or by a suitable sub-workflow. These variations
are achieved by applying special graph-rewriting rules applicable to non-linear
and non-tree workflow graphs.
As for related work, I will mainly discuss the relationships of the efforts above
to the knowledge-discovery ontology project KDOnto [6], the more foundational
(and less procedurally oriented) ontology OntoDM [7], and the relevant achieve-
ments of the recent eLico project. Here, the ontology-based workflow planning
concepts [8] were integrated in the popular software tools Taverna and Rapid-
Miner.
Finally, the talk will cover some selected topics, which I argue to be worth-
while a concentrated investigation in the future. Most imporantly, I will outline
the idea of semantic meta-learning, in which a meta-learner could learn and rea-
son on the basis of the semantics of (object-level) data attributes. I will also
suggest to elaborate a theoretical framework in which performance indicators
for data models (such as classifier accuracies) would be jointly estimated from
validation experiments with the currently analysed data, and from accuracies of
similar models in similar previous experiments.
Acknowledgements. The talk covers my joint work with Monika Zemenová, Petr
Křemen, Jiřı́ Bělohradský, Radomı́r Černoch, Ondřej Kuželka, Matěj Holec, Vid
Podpečan, Igor Trajkovski, Nada Lavrač, David Monge, and C.G. Garino. I
am supported by the Czech Science Foundation through project P103/10/1875
Learning from Theories.
References
1. Zakova, M., Zelezny, F., Garcia-Sedano, J.A., Tissot, C.M., Lavrac, N., Kremen, P.,
Molina, J.: Relational data mining applied to virtual engineering of product designs.
In: Procs. of the 16th Int. Conf. on Inductive Logic Programming (ILP’06), Springer
(2006)
2. Zakova, M., Zelezny, F.: Exploiting term, predicate, and feature taxonomies in
propositionalization and propositional rule learning. In: The 18th Eur. Conf. on
Machine Learning and the 11th Eur. Conf. on Principles and Practice of Knowledge
Discovery in Databases (ECML/PKDD’07), Springer (2007)
3. Zakova, M., Kremen, P., Zelezny, F., Lavrac, N.: Automatic knowledge discovery
workflow composition through ontology-based planning. IEEE Transactions on Au-
tomation Science and Engineering 8 (2011) 253–264
4. Zakova, M., Podpecan, V., Zelezny, F., Lavrac, N.: Advancing data mining workflow
construction: A framework and cases using the orange toolkit. In: Int. Workshop
on Third Generation Data Mining: Towards Service-oriented Knowledge Discovery
(SoKD’09). (2009)
5. Belohradsky, J., Monge, D., Zelezny, F., Holec, M., Garino, C.G.: Template-based
semi-automatic workflow construction for gene expression data analysis. In: Proc.
of the 24th Int. Sympos. on Computer-Based Medical Systems (CBMS’11), IEEE
Computer Society (2011)
6. Diamantini, C., Potena, D., Storti, E.: Kddonto: An ontology for discovery and
composition of kdd algorithms. In: Int. Workshop on Third Generation Data Mining:
Towards Service-oriented Knowledge Discovery (SoKD’09). (2009)
7. Panov, P., Soldatova, L.N., Džeroski, S.: Towards an ontology of data mining inves-
tigations. In: Procs. of the 12th Int. Conf. on Discovery Science (DS’09), Springer
(2009)
8. Hilario, M., Nguyen, P., Do, H., Woznica, A., Kalousis, A.: Ontology-based meta-
mining of knowledge discovery workflows. In: Meta-Learning in Computational
Intelligence. Springer (2011)