James Michaelis

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Data.gov is a website that provides US Government data to the general public to ensure better accountability and transparency. Our recent work on the Data-gov Wiki, which attempts to integrate the datasets published at Data.gov into the Linking Open Data (LOD) cloud (yielding ”linked government data”), has produced 5 billion triples covering a range of(More)
International open government initiatives are releasing an increasing volume of raw government datasets directly to citizens via the Web. The transparency resulting from these releases creates new application opportunities but also imposes new burdens inherent to large-scale distributed data integration, collaborative data manipulation and transparent data(More)
The Open Government Directive is making US government data available via websites such as Data.gov for public access. In this paper, we present a Semantic Web based approach that incrementally generates Linked Government Data (LGD) for the US government. In focusing on the trade-off between high quality LGD generation (requiring non-trivial human expert(More)
As open government initiatives around the world publish an increasing number of raw datasets, citizens and communities face daunting challenges when organizing, understanding, and associating disparate data related to their interests. Immediate and incremental solutions are needed to integrate, collaboratively manipulate, and transparently consume(More)
The Third Provenance Challenge (PC3) offered an opportunity for provenance researchers to evaluate the interoperability of leading provenance models with special emphasis on importing and querying workflow traces generated by others. We investigated interoperability issues related to reusing Open Provenance Model (OPM)-based workflow traces. We compiled(More)
In this paper we describe the application of a novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspace need to be reconciled and managed automatically. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of integrated learning and reasoning (ILR)(More)
Governments around the world have been releasing raw data to their citizens at an increased pace. The mixing and linking of these government datasets enhances their value and makes new insights possible. The use of mashups, i.e., digital works in which data from one or more sources is combined and presented in innovative ways, is a great way to expose this(More)
We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components,(More)
As the Internet of Things (IoT) matures in commercial sectors, the promise of diverse new technologies such as data-driven applications, intelligent adaptive systems, and embedded optimized automation will be realized in every environment. An immediate research question is whether contemporary IoT concepts can be applied also to military battlefield(More)