Authors:
Alexandra Mazak
1
;
Bernhard Schandl
2
and
Monika Lanzenberger
1
Affiliations:
1
Vienna University of Technology, Austria
;
2
University of Vienna, Austria
Keyword(s):
Ontology alignment, Application context, Modeling focus, Heterogeneity coefficient, Mismatch-at-risk metric.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Ontology Matching and Alignment
;
Ontology Sharing and Reuse
;
Symbolic Systems
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 method’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 lis
ts 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 concepts’ 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.
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