Extracting structured data from publications in the Art Conservation Domain. (12th February 2014)

• Record Type: Journal Article
• Authors: Odat, Suleiman; Groza, Tudor; Hunter, Jane
• Is Part Of: Digital scholarship in the humanties. Volume 30:Number 2(2015)
• Page Start: 225
• Page End: 245
• Publication Date: 2014-02-12
• Subjects: Philology -- Data processing -- Periodicals; Computational linguistics -- Periodicals; 410.285
• DOI: 10.1093/llc/fqu002
• Languages: English
• ISSNs: 2055-768X

Abstract

Abstract: 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.