Domain-specific summarisation of Life-Science e-experiments from provenance traces

Abstract

Translational research in Life-Science nowadays leverages e-Science platforms to analyze and produce huge amounts of data. With the unprecedented growth of Life-Science data repositories, identifying relevant data for analysis becomes increasingly difficult. The instrumentation of e-Science platforms with provenance tracking techniques provides useful information from a data analysis process design or debugging perspective. However raw provenance traces are too massive and too generic to facilitate the scientific interpretation of data. In this paper, we propose an integrated approach in which Life-Science knowledge is (i) captured through domain ontologies and linked to Life-Science data analysis tools, and (ii) propagated through rules to produced data, in order to constitute human-tractable experiment summaries. Our approach has been implemented in the Virtual Imaging Platform (VIP) and experimental results show the feasibility of producing few domain-specific statements which opens new data sharing and repurposing opportunities in line with Linked Data initiatives