Paper Info
Title
Partial Ontology Matching Using Instance Features
Abstract
Ontologies are a useful model to express semantics in a machine-readable way. A matching of heterogeneous ontologies is often required for many different applications like query answering or ontology integration. Many systems coping with the matching problem have been developed in the past, most of them using meta information like concept names as a basis for their calculations. This approach works well as long as the pieces of meta information are similar. In case of very differently structured ontologies or if a lot of possible synonyms, homonyms or meaningless meta information are used, the recognition of mappings gets difficult. In these cases instance-based matching methods are a useful extension to find additional correct mappings resulting in an improved matching quality, because instances provide a lot of information about a concept. This paper presents a novel instance-based matching algorithm which calculates different features using instances. These features characterize the concepts and are compared using different similarity functions. Finally, the similarity values are used to determine 1:1 mapping proposals.
Year
DOI
Venue
2009
10.1007/978-3-642-05151-7_32
OTM Conferences (2)
Keywords
Field
DocType
different similarity function,instance features,improved matching quality,partial ontology matching,different application,different feature,meta information,meaningless meta information,useful extension,concept name,similarity value,matching problem,ontology matching
Ontology (information science),Edit distance,Ontology-based data integration,Ontology alignment,Feature vector,Information retrieval,Homonym,Blossom algorithm,Mathematics,Semantics
Conference
Volume
ISSN
Citations 
5871
0302-9743
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Katrin Zaiß121.74
Stefan Conrad2168105.91