Jason Slepicka

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There is a huge amount of data spread across the web and stored in databases that we can use to build knowledge graphs. However, exploiting this data to build knowledge graphs is difficult due to the heterogeneity of the sources, scale of the amount of data, and noise in the data. In this paper we present an approach to building knowledge graphs by(More)
Incorporating structured data in the Linked Data cloud is still complicated, despite the numerous existing tools. In particular, hierarchical structured data (e.g., JSON) are underrepresented, due to their processing complexity. A uniform mapping formalization for data in different formats, which would enable reuse and exchange between tools and applied(More)
Data sets are generated today at an ever increasing rate in a host of new formats and vocabularies, each with its own data quality issues and limited, if any, semantic annotations. Without semantic annotation and cleanup, integrating across these data sets is difficult. Approaches exist for integration by semantically mapping such data using R2RML and its(More)
The Linked Data cloud is an enormous repository of data, but it is difficult for users to find relevant data and integrate it into their datasets. Users can navigate datasets in the Linked Data cloud with ontologies, but they lack detailed characterization of datasets' contents. We present an approach that leverages r2rml mappings to characterize datasets.(More)
A simple but accurate regression method for reducing the conventional single-frame interferograms that primarily arise in flow and heat-transfer measurements is proposed and tested. Phase extraction from the nonlinear interferogram intensity model becomes an ill-posed nonuniqueness problem. Unlike previous regression techniques, the method is based on(More)