1. Ontology Classifications Acknowledgement Abstract Content from simulation systems is useful in defining domain ontologies. We describe a digital library process to generate and leverage domain ontologies to support simulation systems tasks. Workflow ontologies may be used to define compositions of simulation-related services. Simulation model ontologies may be used in customizing collection management systems for tasks such as organization, interface construction, and metadata record generation. Improving Simulation Management Systems through Ontology Generation and Utilization Targeted Simulation Systems Jonathan P. Leidig, Edward A. Fox, Kevin Hall, Madhav Marathe, Henning Mortveit Contact: leidig@vt.edu Simulation Workflows Ontology Generation and Technologies Model Ontology-Utilizing Digital Library Services This work has been partially supported by NSF SDCI Grant OCI-1032677, NSF Nets Grant CNS-062694, CNS-0831633, HSD Grant SES-0729441, CDC Center of Excellence in Public Health Informatics Grant 2506055-01, NIH-NIGMS MIDAS GM070694-05/06, and DTRA CNIMS Grant HDTRA1-07-C-0113. Related Article: Jonathan Leidig, Edward Fox, Kevin Hall, Madhav Marathe, Henning Mortveit. SimDL: A Model Ontology Driven Digital Library for Simulation Systems. ACM/IEEE Joint Conference on Digital Libraries, Ottawa, Canada, June 13-17, 2011. Prototype Implementation & Applications Supported Swiss Tropical Institute  Malaria models  Dataset analysis Cyberinfrastructure Network Science  Network simulations  Network analysis  Content staging  Interface presentation of model parameters  Input parameter gathering  Input configuration generation  Input configuration validation  Input, result, and analysis storing and retrieving  Gathering provenance from workflow stages  Model-specific indexing  Faceted browsing  Ranked searching Ontology Formats  XML schema  RDF Ontology Generation  Human-intensive model ontology generation  Metadata description set generation software  Harmonization yields context-specific ontologies Harmonization  RDF descriptions  Software guided human mapping Ontology Terms  Dublin Core terms  Infrastructure and collection-level terms  5S framework terms  Model and context-specific terms Schema Input Configuration Output Result Dataset Simulation Process Analysis Process Documentation Annotation Experiment Epidemiology Applications  Malaria models  Influenza models  ODE and agent-based models  Models from NIH MIDAS community  Models from Gates Foundation community Analysis applications  Network analysis  Model-specific analysis Digital Library Integration  Institutional infrastructure  Network science cyberinfrastructure Virginia Bioinformatics Institute Biological domains  Infectious diseases (e.g., H1N1, H5N1)  Biological organs Infrastructure domains  Transportation systems  Computer and wireless networks Simulation model ontology Input schema Result schema Validation Compatible analyses Language support Model ontology relationships (e.g., malaria, influenza) Model ontology Model ontology Model ontology Context-specific ontology Context ontology relationships (e.g., epidemiology, network science) Context ontology Context ontology Context ontology Domain-specific meta-ontology Recommending and Selecting Model-Specific Ontologies Model Ontology Harmonization Context-Specific Ontologies Context Ontology Harmonization Domain Meta Ontologies Sample Content Input Files Result Summaries Analyses Result Files Products Model-Specific Description Sets Harmonized Description Sets Example Records (XML, RDF) DB Metadata Schemas (DDL)