Information Modeling and Semantic Web Application For National Climate Assessment Jin Guang Zheng 1 Curt Tilmes 2 Aaron Smith 2 Stephan Zednik 1 Xiaogang Ma 1 Justin Goldsten 2 Peter Fox 1 ( 1 Rensselaer Polytechnic Institute, Troy, NY, 12180, 2 United States Global Change Research Program, Washington DC) Every four years, Earth scientists work together on a National Climate Assessment (NCA) report which integrates, evaluates, and interprets the findings of climate change and impacts on affected industries such as agriculture, natural environment, energy production and use, etc. Given the amount of information presented in each report, and the wide range of information sources and topics, it can be difficult for users to find and identify desired information. To ease the user effort of information discovery, well-structured metadata is needed that describes the report's key statements and conclusions and provide for traceable provenance of data sources used. We present an assessment ontology developed to describe terms, concepts and relations required for the NCA metadata. Wherever possible, the assessment ontology reuses terms from well-known ontologies such as Semantic Web for Earth and Environmental Terminology (SWEET) ontology, Dublin Core (DC) vocabulary, and Global Change Master Directory (GCMD) vocabulary. We have generated sample National Climate Assessment metadata conforming to our assessment ontology by decomposing the document of the report and extracting the meaningful concepts. The metadata are publicly exposed via a SPARQL-endpoint and website. We have also modeled provenance information for the NCA writing activities using the W3C recommendation- candidate PROV-O ontology. Using this provenance the user will be able to trace the sources of information used in the assessment and therefore make trust decisions. In the future, we are planning to implement a faceted browser and keyword based searcher over the metadata to enhance metadata traversal and information discovery. ABSTRACT Poster: MT15A-08 REFERENCE: Sponsors: National Science Foundation THE LINKED DATA OF NCA REPORT USE CASE SCENARIO PROVENANCE USE CASE: The reader of the NCA report wishes to identify the dataset used to generate a particular figure in the report. S/he is directed first to the figure caption. Selecting the caption displays a page of information about the figure, and, if the figure was originally published in another paper, including a link via the paper’s DOI to the publisher’s site describing that paper and offering it for download. The page of information also includes references to the datasets used in the paper on which the figure was based. Following each of the dataset links presents a page of information about the dataset, including links back to the agency/data center web page which provides more detail on the dataset (metadata) and from which the actual data may be available for order or download. Figure 1: An illustration of the provenance use case using the National Climate Assessment 2009 Report, Chapter Southeast. Earth Scientist work together on NCA report, which contains information from wide range of information source, convers many related topics. We collected provenance information for the NCA writing activities. These information are modeled using W3C recomandation-candidate PROV-O ontology. Using these provenance information, user will be able to trace the sources of the information and therefore make the trust decision. We use GCIS Ontology to encode concepts and relations presented in the report. These knowledge are stored in a triple store, a database for the semantic web data. Users can easily navigate and browse the data via the Linked Data API interface. Data are also available in multiple format including RDF and developer-friendly JSON format. We designed GCIS Ontology that defines a set of concepts and relations to model and represent the information of NCA report. We reused as many concepts and relations across different ontologies, including FOAF 1, Dublin Cores 2, SKOS 3, etc. Information presented in the report contains wide range of topics. We use GCMD keywords and other topic keywords to model and tag topic information. In the future, we will design and implement a faceted browser to help users to search and browse the data. The faceted browser will leverage information such as topic keywords.