align++: A Heuristic-based Method for Approximating the Mismatch-at-Risk in Schema-based Ontology Alignment

A. Mazak, B. Schandl, M. Lanzenberger:
"align++: A Heuristic-based Method for Approximating the Mismatch-at-Risk in Schema-based Ontology Alignment";
Vortrag: KEOD 2010 International Conference on Knowledge Engineering and Ontology Development, Valencia (Spain); 25.10.2010 - 28.10.2010; in:"KEOD 2010", J. Dietz et al. (Hrg.); SciTePress, (2010), S. 17 - 26.

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Abstract:


Frequently, ontologies based on the same domain are similar but also have many differences, which are known
as heterogeneity. The alignment of entities which are not meant to be used in the same context, or which
follow different modeling conventions, may cause mismatch in ontology alignment. End-users would benefit
from knowing the risk level of mismatch between ontologies prior to starting a time- and cost-intensive procedure.
With our heuristic-based method align++ we propose to consider the general application context of
a modeled domain (the modeling context) in order to enhance the user support in schema-based alignment.
In the methodĀ“s first part, ontology concepts are enriched with weighting meta-information, resulting from
two indicators: importance weighting indicator and importance outdegree indicator. These indicators contain
model- and graph-based information and can be observed and measured at the schema level of an ontology.
The output of the first part are ranking lists of importance indicators for each ontology concept in the role
of a domain class. In the second part, the candidate sample for our mismatch-risk model bases on external
user input by manually identifying concepts between the lists of each source ontology. The heterogeneity risk
among the conceptsĀ“ importance indicator values is measured as standard deviation over the candidate sample.
Afterwards these measured values are aggregated, and a heterogeneity coefficient is calculated. On the
basis of this risk factor the mismatch-at-risk (MaR) between ontologies can be approximated as a threshold
for schema-based ontology alignment.