Building a semantic knowledge base for painting conservators

The Twentieth Century Paint project is a collaboration between the Asia Pacific Twentieth Century Conservation Art Research Network (APTCCARN) and the eResearch Lab at the University of Queensland. It is a collaborative effort to explore the preservation of twentieth-century paintings in Asia and the Pacific. One of the key objectives is to establish an online knowledge-base that will provide conservators with access to integrated, structured information and a portfolio of experiments and case studies that document the different causes of paint degradation and the optimum conservation treatments. This paper describes the knowledge-base and the associated ontology and services developed by the eResearch Lab in collaboration with APTCCARN - that will enable future expansion of the knowledge base through both harvesting of structured data and collaborative input by domain experts

Extracting structured data from publications in the Art Conservation Domain

The most common method of publishing new discoveries about art conservation techniques and research has been through traditional full-text publications. Such corpora typically only support searching via metadata (e.g. title, authors, or keywords) and full-text. In particular, it is difficult to discover valuable information about the chemical processes, experimental results, or preservation treatments associated with the conservation of paintings from a specific genre. This article addresses this problem by focusing on the extraction of structured data (that complies with a pre-defined ontology) from a distributed corpus of publications about painting conservation. Our specific extraction method involves a unique combination of named entity recognition (using gazetteer-based and machine learning-based methods) followed by relationship extraction (using rule-based and machine learning-based methods). The resulting structured data are stored in a resource description framework triple store, and a Web-based graphical user interface enables the SPARQL querying, retrieval, and display of the search results. The results from applying our techniques to a corpus of publications on art conservation indicate that our approach achieves higher quality precision and recall in extracting named entities and relations from publications, relative to alternative existing approaches