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Ontology-Based Meta-Mining of Knowledge Discovery Workflows

  • Melanie Hilario
  • Phong Nguyen
  • Huyen Do
  • Adam Woznica
  • Alexandros Kalousis
Part of the Studies in Computational Intelligence book series (SCI, volume 358)

Abstract

This chapter describes a principled approach to meta-learning that has three distinctive features. First, whereas most previous work on meta-learning focused exclusively on the learning task, our approach applies meta-learning to the full knowledge discovery process and is thus more aptly referred to as meta-mining. Second, traditional meta-learning regards learning algorithms as black boxes and essentially correlates properties of their input (data) with the performance of their output (learned model). We propose to tear open the black box and analyse algorithms in terms of their core components, their underlying assumptions, the cost functions and optimization strategies they use, and the models and decision boundaries they generate. Third, to ground meta-mining on a declarative representation of the data mining (dm) process and its components, we built a DM ontology and knowledge base using the Web Ontology Language (owl).

The Data Mining Optimization Ontology (dmop, pronounced dee-mope)) provides a unified conceptual framework for analysing dm tasks, algorithms, models, datasets, workflows and performance metrics, as well as their relationships. The dm knowledge base uses concepts from dmop to describe existing data mining algorithms and their implementations in major dm software packages. Meta-data collected from data mining experiments are also described in terms of concepts from the ontology and linked to algorithm and operator descriptions in the knowledge base; they are then stored in data mining experiment data bases to serve as training and evaluation data for the meta-miner.

These three features together lay the groundwork for what we call deep or semantic meta-mining, i.e., dm process or workflow mining that is driven simultaneously by meta-data and by the collective expertise of data miners embodied in the data mining ontology and knowledge base. In Section 1, we review the state of the art in the fields of meta-learning and data mining ontologies; at the same time, we motivate the need for ontology-based meta-mining and distinguish our approach from related work in these two areas. Section 2 gives a detailed description of dmop, while Section 3 introduces a novel method for ontology-based discovery of generalized patterns from data mining workflows. Section 4 reports on proof-of-concept experiments conducted to gauge the efficacy of dmop-based workflow mining, and Section 5 concludes.

Keywords

Data Mining Feature Selection Knowledge Discovery Feature Subset Parse Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Melanie Hilario
    • 1
  • Phong Nguyen
    • 1
  • Huyen Do
    • 1
  • Adam Woznica
    • 1
  • Alexandros Kalousis
    • 1
  1. 1.Artificial Intelligence LaboratoryUniversity of GenevaSwitzerland

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