Linked Data Generation from Digital Libraries

摘要

Knowledge acquisition, modeling and publishing are important in digital libraries with large heterogeneous data sources. The process of extracting, structuring, and organizing knowledge from one or multiple data sources is required to construct knowledge-intensive systems and services for the Semantic Web. This way, the processing of large and originally semantically heterogeneous data sources is enabled and new knowledge is captured. Thus, offering existing data as Linked Data increases its shareability, extensibility and reusability. However, using Linking Data, as a means to represent knowledge, has proven to be easier said than done!