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Debugging inconsistent OWL ontologies is a timeconsuming task. Debugging services included in existing ontology engineering tools are still far from providing adequate support to ontology developers and domain experts for this task, due to their lack of efficiency or precision when explaining the main causes for inconsistencies. We present a catalogue of common antipatterns found in inconsistent ontologies that can be used in combination with these tools to make this task more effective.

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National Geographic Institute of Spain (IGN-E) wanted to integrate its main information sources for building a common vocabulary reference and thus to manage the huge amount of information it held. The main problem of this integration is the great heterogeneity of data sources. The Ontology Engineering Group (OEG) is working with IGN-E to attain this objective in two phases: first, by creating automatically an ontology using the semantics of catalogues sections, and second, by discovering mappings automatically that can relate ontology concepts to database instances. So, these mappings are the instruments to break the syntactic, semantic and granularity heterogeneity gap. We have developed software for building a first ontology version and for discovering automatically mappings using techniques that take into account all types of heterogeneity. The ontology contains a set of extra-attributes which are identified in the building process. The ontology, called PhenomenOntology, will be reviewed by domain experts of IGN-E. The automatic mapping discovery will be also used for discovering new knowledge that will be added to the ontology. For increasing the usability and giving independence to different parts, the processes of each phase will be designed automatically and as upgradeable as possible.

Abstract. Geographical Information is increasingly captured, managed and updated by different cartographic agencies. This information presents different structures and variable levels of granularity and quality. In practice, such heterogeneity causes the building up of multiple sets of geodata with different underlying models and schemas that have different structure and semantics. Ontologies are a proposal widely used for solving heterogeneity and a way of achieving the data harmonization and integration that GIS and SDI need. This paper presents three hydrographical ontologies (which are built using topdown and bottom-up approaches) and an approach to comparing them; the goal of this approach is to prove which ontologies have a better coverage of the domain. In order to compare the resultant ontologies, six qualitative facets have been studied: sources used (amount, richness and consensus), reliability of building approaches (community extending use, recommendations), ontology richness (number and types of components), formalization (language), granularity (scale factor) and the design criteria followed.