Ontology-Driven Data Preparation for Data Mining Martin Zeman, KSI MFF UK Martin Ralbovský, KIZI FIS VŠE.

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Presentation transcript:

Ontology-Driven Data Preparation for Data Mining Martin Zeman, KSI MFF UK Martin Ralbovský, KIZI FIS VŠE

Possible usage of domain ontologies in the KDD process Knowledge discovery x knowledge storage Data understanding phase Knowledge from ontology helps to comprehend the domain Task design phase Define meaningful tasks with aid of ontology Result interpretation phase How do KDD results cope with ontology knowledge

Previous works Theoretically high (methodology) Practically low  (manual experiments, no real software support) Main goal: software support for some of the ontology support ideas Implementation platform: Ferda

How to load ontology? 1 st problem: how to load ontology? Ontology language – OWL 1.1 Available software usage – OWL API Technical situation Ferda -.NET + ICE Middleware OWL API – Java

How to load ontology? Ontology Module OWL API Java Ontology Box Java.NET ICE Box API.NET

Mapping 2 nd problem: how to connect ontology and database? Columns Table or database Classes and instance Mapping Relation- 1:N, M:1, M:N?

Creation of attributes Proper categorization of domains – crucial step for successful KDD (not only in GUHA) Example: blood pressure above 140/90 mm Hg is considered as hypertension Categorization information available in ontology?

Additional information Cardinality (nominal/ordinal/ordinal cyclic/cardinal) Maximum Minimum Domain dividing values Distinct values Saving information to ontology Datatype properties Domain: metaclass owl:class Advantages Inherent part of the domain Reusability Not restricted to KDD (GUHA)

Diastolic blood pressure

Attribute creation algorithm IF (cardinality == nominal OR cardinality == ordinal cyclic) each value one category return ELSE IF (count of categories <= 5) each value one category return ELSE find the domain range (minimum, maximum) IF (exist domain dividing values) split according domain dividing values IF (exist distinct values) create category for each distinct value

Identification of semantically related attributes Analytical question: “What is the relation between blood pressure levels and hypertension?” What are the attributes corresponding to blood pressure/hypertension? Boxes asking for creation mechanism can help Experiment

Conclusions Implemented support for: Mapping ontology and database concepts Semi – automatic creation of right categorization Identification of related attributes