Transformation of Transportation Data Models from Unified Modeling Language to Web Ontology Language

(FGDC) defined the transportation geographic information framework data content standard using UML class diagrams. Many transportation applications also developed data models using UML class diagrams (e.g., 6, 7 ). The UML data model supports the exchange of spatial and temporal transportation data and fosters improvements in common spatial data infrastructure through enhanced data sharing. Furthermore, as an object-oriented data model, it enhances the efficiency of database maintenance and increases application performance (6). Despite these advantages, the UML data model supports data interoperability only at the syntactic and schematic level and cannot resolve the semantic heterogeneity problem that is inherent in diverse transportation data sources. Without agreement regarding the semantics of their data, different transportation applications and organizations have used different terminologies to develop their own UML data models. Thus, the UML data model cannot be presented as a semantically coherent framework at the transportation domain for the integration of distributed and heterogeneous data sources (7 ). One possible approach to overcome the problem of semantic heterogeneity is by means of ontology (8). Literatures using ontology to address the vexing semantic challenges in transportation applications appeared recently. For example, Wang et al. propose an ontologybased public transport query system to query semantic information (9). Obitko and Mařík present a way of expressing mappings between ontologies and using such mappings for communication between transportation agents (10). Darter et al. report the development of an interoperable asset management system using ontology-based semantic models (11). Sudre et al. describe the creation of bridge ontologies to address the lack of interoperability that transportation agencies face (12). Although recognized as a means to overcome the problem of semantic heterogeneity in transportation applications, ontology is new and is immature; many related issues are still in the research stage, such as ontology development (13). Thus, although people have begun to actively build ontologies for transportation applications [e.g., the GDF-based Ontology of Transportation Networks (OTN), www.pms.ifi.lmu.de/rewerse-wga1/otn/OTN.owl], ontology building is not an easy task. Currently, ontologies are built by a few people, typically researchers, using ontology tools and editors such as Protégé (14). Although these ontology tools and editors supporting ontological modeling have improved over the past few years and many functions (e.g., ontology consistency checking, importation of existing ontologies, and ontology visualization) are now available, manual ontology building has been a difficult and error-prone task and becomes the bottleneck in ontology-acquisition processes. For instance, it is unrealistic that Transformation of Transportation Data Models from Unified Modeling Language to Web Ontology Language