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An Exploration Of Understanding Heterogeneity Through Data Mining

An Exploration Of Understanding Heterogeneity Through Data Mining

ABSTRACT
Development of internet andWeb have resulted in many distributed
information resources which in general are structurally and semantically
heterogeneous even in the same domain. However, heterogeneity
itself has not been studied in a formal way so that the representation
of different kinds of heterogeneities can be generically
processed by other programs automatically. Most descriptions and
categorization schemes of heterogeneities were given in languages
specific to different research groups. We believe that efforts invested
in a thorough research of heterogeneity can ultimately benefit
both data integration and data mining communities. In this paper
we give a brief survey of various ways to categorize heterogeneity
in the literature, and then performed a case study on detecting
a specific class of heterogeneity in the setting of Semantic Web
ontologies–the one that can be discovered by only data-driven approaches.
Finally we propose an automatic ontology matching system
that can detect this heterogeneity by using redescription mining
techniques. We also believe that automatic ontology matching
process is a helpful step in tasks of mining multiple information
sources in the heterogeneous scenario.
Categories and Subject Descriptors
H.2.8 [Database applications]: Data mining—distributed data mining;
I.2.4 [Knowledge Representation Formalism and Methods]:
Ontology; H.3.5 [Information Systems]: Information Storage and
Retrieval—data integration
General Terms
Theory, Design
Keywords
Heterogeneity, Ontology Matching, Redescription Mining
1. INTRODUCTION
In both data integration and data mining communities, problems
that might arise due to heterogeneity of multiple data resources are
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