Embedding Data within Knowledge Spaces

Myers, James D., Futrelle, Joe, Gaynor, Jeff, Plutchak, Joel, Bajcsy, Peter, Kastner, Jason, Kotwani, Kailash, Lee, Jong Sung, Marini, Luigi, Kooper, Rob, McGrath, Robert E., McLaren, Terry, Rodriguez, Alejandro, Liu, Yong

arXiv.org Artificial Intelligence 

The promise of e-Science will only be realized when data is discoverable, accessible, and comprehensible within distributed teams, across disciplines, and over the long-term - without reliance on out-of-band (non-digital) means. We have developed the open-source Tupelo semantic content management framework and are employing it to manage a wide range of e-Science entities (including data, documents, workflows, people, and projects) and a broad range of metadata (including provenance, social networks, geospatial relationships, temporal relations, and domain descriptions). Tupelo couples the use of global identifiers and resource description framework (RDF) statements with an aggregatable content repository model to provide a unified space for securely managing distributed heterogeneous content and relationships. The Tupelo framework includes an HTTPbased data/metadata management protocol, application programming interfaces, and user interface widgets which have been incorporated into NCSA's portal and workflow tools and is a key component in recent work creating dynamic digital observatories (digital watersheds) that combine observational and modeled information. Tupelo also supports specialized indexes and inference logic (computation) relevant to metadata including geospatial location and provenance. This additional capability creates a powerful knowledge space that can map between disciplinary conceptual models and between the storage and data organization choices made by different e-Science organizations.

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