Concept Level Matching of Geospatial Ontologies

1. GIS Planet 2005, Estoril, Portugal Concept Level Matching of Geospatial Ontologies A methodology and tool support for semi-automatic alignment of heterogeneous geospatial ontologies University of Illinois at Chicago Department of Computer Science Isabel F. CRUZ, William G. SUNNA and Kalyan AYLOO

2. Objective To bridge the semantic gap among distributed heterogeneous geospatial data • We assume that there is an ontology associated with each database • The ontology describes the hierarchical relationship between the database concepts • We alignthese heterogeneous ontologies by establishing mappings between related concepts in the ontologies • We applied our approach on two distributed architectures

3. Centralized integrated system • In a centralized integrated system, a global ontology is introduced • Each distributed database that wishes to participate in the integrated system must have its ontology aligned with the global ontology • We refer to the ontologies of the distributed databases as distributed or local ontologies • The global ontology can be designed so as: • To encompass as much as possible the information contained in the distributed ontologies • To become a standard for that domain Figure 1: Centralized integrated system

4. Peer-to-peer integrated system • In this architecture, the ontology of every peer system is aligned with other peers in the network • A query posed on one of the databases (the target database) is propagated to the others in the network Figure 2: Five databases participating in a peer-to-peer integrated system

5. Geospatial ontologies • For centralized architecture, we consider an example on land use codes for the state of Wisconsin • The distributed ontologies were separately developed and are quite dissimilar • For a peer-to-peer architecture, we concentrate on wetland classifications • In particular, we concentrate on an established standard, the "Cowardin'' Wetland Classification [5]. • We will use this standard together with its variant, the South African Wetland Classification Inventory [7] and establish their alignment

6. Heterogeneity in Land Use Codes Synonyms Two Land Use Codes

7. Heterogeneity in Wetland Classifications • Organizations monitoring the wetlands data inventory have an interest in sharing data • The lack of standard classification has long been identified as an obstacle to the development, implementation, and monitoring of wetland conservation strategies [7] • In defining wetlands, the United States adopts the "Cowardin'' Wetland Classification System [5]. • In contrast, European nations use the International Ramsar Convention Definition (http://www.ramsar.org), and South Africa uses the National Wetland Classification Inventory [7]

8. Heterogeneity in Wetland Classifications Figure 4: "Cowardin" Wetland Classification System. Figure 5: South African National Wetland Inventory

9. Ontology Alignment • Ontology alignment is the process of identifying related concepts in the aligned ontologies that match in definition and purpose of use • We represent the ontologies as trees. • Each tree contains vertices that can be potentially mapped to vertices in another tree Figure 6: Mapping related vertices

10. Mapping Types • Once two concepts are known to be aligned, the nature of the relation between them can be characterized using the following mapping types: • Exact, the connected vertices are semantically equivalent in definition and purpose of use • Approximate, the connected vertices are semantically approximate in meaning and purpose of use • Null, the vertex in a particular ontology does not have a semantically related vertex in the other ontology with which it can be matched • Superset, A vertex in one ontology is more general in meaning than a vertex or a set of vertices together in the other ontology • Subset, the vertex or a set of vertices together in one ontology are less general in meaning than the vertex in the other ontology Figure 6: Mapping related vertices

11. Semi-automatic Alignment • Framework that defines the values associated with the vertices of the ontology as functions of the: • values of the children vertices, or • user input • User (or system) establishes some mapping types • System propagates the mapping types along the ontologies (bottom-up) as much as possible

12. Propagation Rules

13. Semi-automatic Alignment Example Mapping established by the system Mapping established by the user

14. Agreement Maker • Visual interface for mapping vertices of aligned ontologies • Existing mappings displayed to the user • Displayed list of mappings updated as user identifies more mappings Figure 9: interface showing established mappings

15. Deduction Results Interface Automatically mapped Figure 10: The mappings after running the deduction process

16. Experimental Results • For our experiment we chose to align the "Cowardin'' Wetland Classification System shown in Figure 4 with the South African National Wetland Classification Inventory shown in Figure 5 • We performed the mappings in cycles, where each cycle consists of two phases: • Manual mappings by the user • Automatic mappings performed by the system when the deduction process is invoked

17. Experimental Results • We define the following metrics • MBU: Number of mappings performed by the user in the first phase of a mapping cycle. • MBS: Number of automatic mappings performed by the system in the second phase of a mapping cycle. • DR: Deduction ratio, that is, the ratio of the number of mappings performed by the system to the number of mappings performed by the user in a mapping cycle. • CDR: Cumulative deduction ratio, that is, the ratio of the number of mappings performed by the system to the number of mappings performed by the user from the first to the current mapping cycle. 18% of the mappings were performed automatically

18. References • Benetti H., Beneventano D., Bergamaschi S., Guerra F., and Vincini M., An Information Integration Framework for E-Commerce. IEEE Intelligent Systems, 17:18-25, 2002. • Bergamaschi S., Guerra F., and Vincini M., A Data Integration Framework for E-Commerce Product Classification. In 1st International Semantic Web Conference (ISWC), pages 379-393, 2002. • Bishr Y., Overcoming the Semantic and Other Barriers to GIS Interoperability. International Journal of Geographical Information Science, 12:299-314, 1998. • Calì A., Calvanese D., Giacomo G. D., and Lenzerini M., Accessing Data Integration Systems through Conceptual Schemas. In 20th International Conference on on Conceptual Modeling - ER, pages 270-284, 2001. • Cowardin L. M., Carter V., Golet F. C., and LaRoe E. T., Classification of Wetlands and Deepwater Habitats of the United States, 1979. http://www.npwrc.usgs.gov/resource/1998/classwet/classwet.htm. • Cruz I. F., Rajendran A., Sunna W., and Wiegand N., Handling Semantic Heterogeneities using Declarative Agreements. In International ACM GIS Symposium, pages 168-174, 2002. • Dini J., Cowan G., and Goodman P., South African National Wetland Inventory, 1998. http://www.ngo.grida.no. • Fonseca F., Egenhofer M., Agouris P., and Camara G., Using ontologies for integrated geographic information systems, GIS 6 (3): 231-257, 2002. • Fonseca F. T. and Egenhofer M. J., Ontology-driven geographic information systems. In ACM-GIS, pages 14-19, 1999. • Gennari J., Musen M., and Park J., Mappings for Reuse in Knowledge-based Systems. In 11th Workshop on Knowledge Acquisition, Modeling and Management, KAW 98, 1998. • Hernandez M. A., Miller R. J., Haas L. M., Yan L., Ho C. T. H., and Tian X., Clio: A Semi-Automatic Tool for Schema Mapping. In SIGMOD Record, page 607, 2001. • Hovy E., Combining and Standardizing Large-Scale, Practical Ontologies for Machine Translation and Other Uses. In First International Conference on Languages Resources and Evaluation (LREC), 1998. • McGuinness D. L., Fikes R., Rice J., and Wilder S., An Environment for Merging and Testing Large Ontologies. In Seventeenth International Conference on Principles of Knowledge Representation and Reasoning (KR-2000), pages 483-493, 2000. • Miller G. A., WordNet: An Online Lexical Database. Technical report, Princeton University, 1990. • Miller R., Hernandez M. A., Haas L. M., Yan L., Ho C. H., Fagin R., and Popa L., The Clio Project: Managing Heterogeneity. ACM SIGMOD Record, pages 78-83, 2001. • Noy N. F. and Musen M. A., PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. In The Sixteenth National Conference on Artificial Intelligence (AAAI), pages 450-455, 2000. • Rahm E. and Bernstein P. A., A survey of approaches to automatic schema matching. VLDB Journal: Very Large Data Bases, 10(4):334-350, 2001.

19. Thank you questions