Search (3 results, page 1 of 1)

  • × author_ss:"Chen, S.-J."
  • × year_i:[2010 TO 2020}
  1. Chen, S.-J.: Semantic enrichment of linked archival materials (2019) 0.01
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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.
  2. Chen, S.-J.: Semantic enrichment of linked personal authority data : a case study of elites in late imperial China (2019) 0.01
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    Abstract
    The study uses the Database of Names and Biographies (DNB) as an example to explore how in the transformation of original data into linked data, semantic enrichment can enhance engagement in digital humanities. In the preliminary results, we have defined instance-based and schema-based categories of semantic enrichment. In the instance-based category, in which enrichment occurs by enhancing the content of entities, we further determined three types, including: 1) enriching the entities by linking to diverse external resources in order to provide additional data of multiple perspectives; 2) enriching the entities with missing data, which is needed to satisfy the semantic queries; and, 3) providing the entities with access to an extended knowledge base. In the schema-based categories that enrichment occurs by enhancing the relations between the properties, we have identified two types, including: 1) enriching the properties by defining the hierarchical relations between properties; and, 2) specifying properties' domain and range for data reasoning. In addition, the study implements the LOD dataset in a digital humanities platform to demonstrate how instances and entities can be applied in the full texts where the relationship between entities are highlighted in order to bring scholars more semantic details of the texts.
  3. Chen, S.-J.; Lee, H.-L.: Art images and mental associations : a preliminary exploration (2014) 0.01
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    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