Throughout the twentieth century, artists in Australia and across the Southeast Asia-Pacific 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 20th 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. They need to access: historical and provenance data associated with individual paintings; information about artistic techniques; paint chemistry databases; publications on preservation treatments and previous research; and collaborative, but secure Web-based tools for capturing, sharing and discussing condition reports, deterioration mechanisms, and characterisation/imaging data (e.g., Scanning Electron Microscopy, Transmission Electron Microscopy, Fourier Transform Infrared Spectroscopy, and X-Ray Diffraction). The aim of this research project is to develop and apply the latest information integration, data management and Semantic Web technologies to build an effective, scalable, extensible, flexible and portable knowledge-base for 20th century art/paint preservation using an approach that enhances the discoverability and re-use of knowledge.
The aims of this project are to develop an e-Research platform for art conservators by tackling the following steps/objectives: • Develop an Ontology of Paintings and PReservation of Art (OPPRA) that will link and integrate terms from standard and disciplinary ontologies (e.g., CIDOC-CRM, OreChem and OAI-ORE) with existing, relevant thesauri (e.g., Getty Art and Architecture Thesaurus and CAMEO: Conservation and Art Material Encyclopedia Online) and new ontologies (e.g., describing types of paint deterioration);
• 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 free-text 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.