Abstract
The development of digital library increases the need of integrating, enriching and republishing library data as Linked Data. Linked library data could provide high quality and more tailored service for library management agencies as well as for the public. However, even though there are many data sets containing metadata about publications and researchers, it is cumbersome to integrate and analyze them, since the collection is still a manual process and the sources are not connected to each other upfront. In this paper, we present an approach for integrating, enriching and republishing library data as Linked Data. In particular, we first adopt duplication detection and disambiguation techniques to reconcile researcher data, and then we connect researcher data with publication data such as papers, patents and monograph using entity linking methods. After that, we use simple reasoning to predict missing values and enrich the library data with external data. Finally, we republish the integrated and enriched library data as Linked Data.
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Miao, Q. et al. (2016). LD2LD: Integrating, Enriching and Republishing Library Data as Linked Data. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_16
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DOI: https://doi.org/10.1007/978-981-10-3168-7_16
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