6533b851fe1ef96bd12a98ff

RESEARCH PRODUCT

Representing and Reasoning for Spatiotemporal Ontology Integration

Nacéra BennacerNadine CullotChristelle VangenotAnastasiya SotnykovaMarie-aude Aufaure

subject

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Information retrieval[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO]Computer scienceOntologyProcess ontologyOntology-based data integrationSuggested Upper Merged OntologyIntegration[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]Spatio-Temporal data02 engineering and technologyOntology (information science)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Open Biomedical OntologiesMapping020204 information systemsOntology components0202 electrical engineering electronic engineering information engineeringUpper ontology020201 artificial intelligence & image processingOntology alignment

description

International audience; The World-Wide Web hosts many autonomous and heterogeneous information sources. In the near future each source may be described by its own ontology. The distributed nature of ontology development will lead to a large number of local ontologies covering overlapping domains. Ontology integration will then become an essential capability for effective interoperability and information sharing. Integration is known to be a hard problem, whose complexity increases particularly in the presence of spatiotemporal information. Space and time entail additional problems such as the heterogeneity of granularity used in representing spatial and temporal features. Spatio-temporal objects possess intrinsic characteristics that make then more complex to handle, and are usually related by specific relationships such as topological, metric and directional relations. The integration process must be enhanced to tackle mappings involving these complex spatiotemporal features. Recently, several tools have been developed to provide support for building mappings. The tools are usually based on heuristic approaches that identify structural and naming similarities [1]. They can be categorized by the type of inputs required for the analysis: descriptions of concepts in OBSERVER [2], concept hierarchies in iPrompt and AnchorPrompt [3] and instances of classes in GLUE [4] and FCA-Merge [5]. However, complex mappings, involving spatiotemporal features, require feedback from a user to further refine proposed mappings and to manually specify mappings not found by the tools.

https://hal-supelec.archives-ouvertes.fr/hal-00265360