Semantic Enrichment of Linked Archival Materials

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.