Ontology Alignment for the Semantic Integration of Heterogeneous Geospatial Data Sets

1. Ontology Alignment for the Semantic Integration of Heterogeneous Geospatial Data Sets Isabel F. Cruz Department of Computer Science University of Illinois at Chicago ifc@cs.uic.edu

2. Open Research Issues • Introduction of Layered Similarity Alignment Approach • A way of capturing wider range of semantic heterogeneities • A way to refine queries by using multiple matching criterion • Introduction of new mapping types between concepts • A way of capturing the relationships between concepts accurately for querying purposes

3. Similarity Alignment Layers • We define 4 layers of ontology alignment: • Alignment by definition (automatic) • Alignment by domain expert (manual) • Alignment by context (automatic) • Alignment by consolidation (automatic)

4. Alignment by definition • Concepts in the ontologies are matched based on the similarity of their definitions. For this purpose, dictionaries will be used to determine measures of such similarities. Example of retrieving terms from two dictionaries with definitions similar to a given definition (first row in the table) with assigned similarity scores

5. Alignment by domain expert • A domain expert will manually match concepts in the heterogeneous ontological tree with each other, especially those concepts that have not been automatically mapped in the definition alignment layer. • The match criteria used to map concepts is determined by the user based on his knowledge of the domain and his best judgment.

6. Alignment by context • Mappings of certain concepts are automatically deduced from the mappings of their children which were determined in the definition alignment layer, the manual alignment layer, and/or the context alignment layer. Subset Production Manufacturing Automatically deduced Electrical Supplies Auto Parts Aero and Auto parts Electrical Supplies Subset Exact

7. Alignment by consolidation • After establishing three separate layers of mappings, an automatic consolidation procedure will use the mappings generated in the previous alignment layers to produce a final layer. The domain expert can specify which layers can participate in the generation of the final mapping layer. • The consolidation module must make decisions on how to resolve contradictions when for example the user establishes a mapping between concepts and the context deduction module establishes a different kind of mappings between the same concepts. To resolve the situation, the domain expert will be given a chance to specify a favoritism sequence for the consolidation module to follow. For example, the domain expert can specify the following sequence: {Manual Mapping, Context Mapping, Definition Mapping}; In case of contradiction between Context Mapping and Manual Mapping, the consolidation module will consider Manual Mapping in producing the final layer

8. New set of mapping types • Exact • The connected concepts are semantically equivalent in definition and purpose of use • Subset • The concept or a set of concepts together in one ontology are less general in meaning than the vertex in the other ontology • Subset complete • One or more concepts in one ontology map completely to one concept in the other ontology A A B B

9. New set of mapping types • Superset • A concept in one ontology is more general in meaning than a concept or a set of concepts together in the other ontology • Superset complete • One concept in one ontology maps completely to a set of concepts in the other ontology A A B B

10. New set of mapping types • Comparative: • Concepts intersect in meaning or in what they contain: • Comparatively exact • Comparatively subset • Comparatively superset • No mapping • the vertex in a particular ontology does not have a semantically related vertex in the other ontology with which it can be matched A B C D E A B C E F A B C DE F G A B C DE H I J K A B C D E H I J K A B C D E F G