SemNExT: A Framework for Semantically Integrating and Exploring Numeric Analyses

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Combining statistical techniques with semantic data representations holds the potential to enhance understandability of scientific results. It can augment scientific findings with existing data sources in a reproducible manner through provenance capture, as well as enable further analysis and deduction through computer and human understandable definitions of terms. We present a framework for semantically integrating and exploring numerical analyses. We call our work SemNExT for Semantic Numeric Exploration Technology. We apply our approach to data analysis aimed at improving understanding of human brain development that leverages the Cortecon RNA-Seq data repository. Our approach supports enrichment of Cortecon data through combinations with structured data sources available via SQL or SPARQL from the web to provide semantically enhanced analyses combined with statistical analyses. Our results are encoded as RDF graphs that may be used as input to reasoners and may drive provenance-aware visualizations. We introduce our infrastructure, describe its use on transcriptomic data analysis of a model of cerebral cortex development, and discuss some emerging suggestions for best practices and future research challenges.


DateCreated ByLink
October 8, 2015
Evan W. PattonDownload

Related Projects:

SemNExT LogoSemantic Numeric Exploration Technology (SemNExT)
Principal Investigator: Deborah L. McGuinness and Kristin Bennett
Description: SemNExT combines numeric analysis of data with semantic understanding and explanation technologies to provide a holistic means of exploring robust datasets.

Related Research Areas:

Data Frameworks
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Description: None.
Knowledge Provenance
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Description: Knowledge Provenance
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Lead Professor: Peter Fox
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Lead Professor: Peter Fox
Description: In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
Concepts: Semantic Web,