Abstract
Matching between concepts describing the meaning of services representing heterogeneous information sources is a key operation in many application domains, including web service coordination, data integration, peer-to-peer information sharing, query answering, and so on. In this paper we present an evaluation of an ontology matching approach, specifically of structure-preserving semantic matching (SPSM) solution. In particular, we discuss the SPSM approach used to reduce the semantic heterogeneity problem among geo web services and we evaluate the SPSM solution on real world GIS ESRI ArcWeb services. The first experiment included matching of original web service method signatures to synthetically alterated ones. In the second experiment we compared a manual classification of our dataset to the automatic (unsupervised) classification produced by SPSM. The evaluation results demonstrate robustness and good performance of the SPSM approach on a large (ca. 700 000) number of matching tasks.
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Notes
Available as open source software at http://semanticmatching.org/
Examples of individual approaches addressing the matching problem can also be found on http://www.ontologymatching.org.
Element level matching techniques compute correspondences by analyzing concepts in isolation, ignoring their relationships with other concepts. In turn, structure level matching techniques compute correspondences by analyzing relationships between concepts considering the structure of each ontology.
SAT4J: a satisfiability library for Java. http://www.sat4j.org/
To obtain this result, we used a modified version of the SMatch package, release of 2010-10-09, available at http://semanticmatching.org/download.html: we set the Cost of each tree edit distance operation to 1.0 in the TreeEditDistance.java class, we configured the edit distance matcher threshold to 0.7 (MatcherLibrary.MatcherLibrary.stringMatchers.EditDistanceOptimized.threshold = 0.7) in the configuration file s-match-spsm-function.properties, and we configured the resulting matching file to show the similarity value (MappingRenderer=it.unitn.disi.smatch.renderers.mapping. SimpleXMLMappingRenderer) in the configuration file s-match-spsm-function.properties.
The edit-distance between two strings is given by the minimum number of operations needed to transform one string into the other, where the operation is an insertion, deletion, or substitution of a single character.
The BROWN CORPUS contains 1 million words, so the probability of obtaining a related word is relatively low. If, for example, a word had 100 related terms, the probability to have a related term is 1/10000. So we could say that the replacement is indeed with a “probabilistically unrelated word”.
The empirical rules have been designed one by one for each of the four alteration operations. The rationale behind these empirical rules is that the change rate discriminates clearly between the cases. Reduction by 0.5 turned out to suffice based on some empirical preliminary testing.
The evaluation could be refined by considering the asymmetry of the similarities [54, 43]; since similarities are asymmetric (hypernyms are usually considered less similar to hyponyms than the other way round), this empirical reduction could be 2-valued, depending on whether the relation changes to less general or more general. This line is viewed as future work.
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Acknowledgements
We thank Fausto Giunchiglia, Fiona McNeill, Mikalai Yatskevich and Aliaksandr Autayeu for many fruitful discussions on the structure-preserving semantic matching. This work has been partly supported by the FP6 OpenKnowledge European STREP project (FP6-027253). The second author appreciates support from the Trentino as a Lab (TasLab) initiative of the European Network of the Living Labs at Informatica Trentina.
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Vaccari, L., Shvaiko, P., Pane, J. et al. An evaluation of ontology matching in geo-service applications. Geoinformatica 16, 31–66 (2012). https://doi.org/10.1007/s10707-011-0125-8
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DOI: https://doi.org/10.1007/s10707-011-0125-8