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Abstract

This paper reports on the preliminary findings of a study that explores mental associations made by novices viewing art images. In a controlled environment, 20 Taiwanese college students responded to the question "What does the painting remind you of?" after viewing each digitized image of 15 oil paintings by a famous Taiwanese artist. Rather than focusing on the representation or interpretation of art, the study attempted to solicit information about how non-experts are stimulated by art. This paper reports on the analysis of participant responses to three of the images, and describes a12-type taxonomy of association emerged from the analysis. While 9 of the types are derived and adapted from facets in the Art & Architecture Thesaurus, three new types - Artistic Influence Association, Reactive Association, and Prototype Association - are discovered. The conclusion briefly discusses both the significance of the findings and the implications for future research.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Abstract

By using the metadata for the fonds of "Chen Cheng-po's Paintings and Documents" (CCP) in the database of the Archives of the Institute of Taiwan History (IHT, Academia Sinica, Taiwan), we develop and enhance a semantic data model for converting the data into a linked data project, focusing on data modeling, data reconciliation, and data enrichment. The research questions are: 1) How can we keep the original rich and contextual information of the archival materials during a LOD task?; 2) How can we integrate heterogeneous datasets about the same real-world resources from libraries, archives, and museums, while keeping the different views distinct?; and, (3) How can we provide added value for semantic metadata of archives in terms of instance-based and schema-based types of enrichment? The project adopts the Europeana Data Model (EDM) as the main model and extends the properties to fit the contextual characteristics of archival materials. Various methods are explored to preserve the hierarchical structure and context of the archival materials, to enrich semantic data, and to connect data from different sources and institutions. We propose four approaches to enriching data semantics by: 1) directly using external vocabularies; 2) reconciling local links to other linked data sources; 3) introducing contextual classes for the appropriate contextual entities; and, 4) utilizing named entity extraction. The results can contribute to the best practice for developing linked data for art-related archival materials.