PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Ontology Matching
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Bioinformatics
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
Instance-based matching of large life science ontologies
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
On matching large life science ontologies in parallel
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
Journal of Biomedical Informatics
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Biological ontologies are widely used for genome annotation. Identifying correspondences between concepts of two ontologies (mapping) allows the reuse and sharing of annotations. Accordingly, biological ontology mapping has attracted a lot of interest. In this paper, we introduce O'Browser, a semi-automatic method for mapping two functional hierarchies using two sets of carefully annotated proteins. While being based on a classical ontology mapping architecture, O'Browser computes correspondences using a combination of different kinds of matchers. A key feature of O'Browser is that it places the expert at the center of the mapping process at two stages: (i) both to validate the very strong correspondences discovered by the system and to identify functional groups of concepts and (ii) to validate the correspondences given by the combination of results found by the matchers. These matchers have been designed in O'Browser to fit best with functional hierarchy features. For instance, we have introduced a new instance-based matcher which uses homology relationships between proteins. The combination of the different matchers is based on an original notion of adaptive weighting. Here, we show the ability of O'Browser to map concepts of Subtilist to concepts of FunCat, two functional hierarchies. First results appear to be very promising.