LogMap: Logic-based and Scalable Ontology Matching

LogMap: Logic-based and Scalable Ontology Matching
LogMap: Logic-based and Scalable Ontology Matching
Bibliographical Metadata
Subject:	Ontology Matching
Year:	2011
Authors:	Ernesto Jim�enez-Ruiz Property "Has authors" (as page type) with input value "Ernesto Jim�enez-Ruiz" contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process. , Bernardo Cuenca Grau, Abraham Bernstein
Venue	ISWC
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Abstract

In this paper, we present LogMap, a highly scalable ontology matching system with built-in reasoning and diagnosis capabilities. To the best of our knowledge, LogMap is the only matching system that can deal with semantically rich ontologies containing tens (and even hun-dreds) of thousands of classes. In contrast to most existing tools, LogMap also implements algorithms for on the fly unsatisability detection and repair. Our experiments with the ontologies NCI, FMA and SNOMEDCT confirm that our system can efficiently match even the largest existing bio-medical ontologies. Furthermore, LogMap is able to produce a `clean' set of output mappings in many cases, in the sense that the ontology obtained by integrating LogMap's output mappings with the input ontologies is consistent and does not contain unsatisable classes.

Conclusion

In this paper, we have presented LogMap, a highly scalable ontology matching tool with built-in reasoning and diagnosis capabilities. LogMap's features and scalability behaviour make it well-suited for matching large-scale ontologies. LogMap, however, is still an early-stage prototype and there is plenty of room for improvement.

Future work

We are currently working on further optimizations, and in the near future, we are planning to integrate LogMap with a Protege-based front-end, such as the one implemented in our tool ContentMap.

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