No abstract

Throughout the twentieth century, artists in Australia and across the Southeast AsiaPacific region have enthusiastically embraced new materials (synthetic media, new pigments, dyes and additives). But compared to traditional artists' paints, these new materials have affected paint handling and paint stability. These new materials have also resulted in a lack of understanding of the preservation issues associated with the resulting artworks. As a result, today's collectors, curators and conservators are confronted with significant material-based preservation questions associated with 20 th century art/paint preservation -but they lack the sustained and integrated knowledge-base to inform their decision making.In order to understand the causes of paint degradation and the best preservation and treatment approaches, conservators need access to a wide range of distributed and cross-disciplinary datasets.  Use a number of case studies to evaluate OPPRA's ability to capture the detailed workflows and outputs associated with paint conservation experiments (e.g., sampling method, experimental processes and characterisation data); Apply and optimise a combination of semantic tagging and machine learning approaches to extract structured knowledge (compliant with OPPRA) from freetext publications on paint conservation -so it can be shared, integrated, compared and re-used; Evaluate OPPRA's ability to integrate experimental datasets, structured knowledge extracted from free-text publications and external public relevant databases (e.g., on paint chemistry), to answer a set of advanced, exemplar (SPARQL) queries specified by art conservators; Evaluate OWL-DL for inferencing and extracting new facts from the integrated knowledge base (generated from integrating experimental data capture, structured knowledge extracted from past publications and public relevant databases) in order to answer advanced queries specified by art conservators.4

No abstract