Abstract
Digital libraries build on classifying contents by capturing their semantics and (optionally) aligning the description with an underlying categorization scheme. This process is usually based on human intervention, either by the content creator or a curator. As such, this procedure is highly time-consuming and - thus - expensive. In order to support the human in data curation, we introduce an annotation tagging system called “AnnoTag”. AnnoTag aims at providing concise content annotations by employing entity-level analytics in order to derive semantic descriptions in the form of tags. In particular, we are generating “Semantic LOD Tags” (linked open data) that allow an interlinking of the derived tags with the LOD cloud. Based on a qualitative evaluation on Web news articles we prove the viability of our approach and the high-quality of the automatically extracted information.
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Notes
- 1.
LibraryThing Tags https://blog.librarything.com/main/category/tags/.
- 2.
- 3.
AnnoTag Website https://spaniol.users.greyc.fr/research/AnnoTag/.
- 4.
Harvard Dataverse News Articles https://doi.org/10.7910/DVN/GMFCTR.
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Kumar, A., Spaniol, M. (2021). AnnoTag: Concise Content Annotation via LOD Tags derived from Entity-Level Analytics. In: Berget, G., Hall, M.M., Brenn, D., Kumpulainen, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2021. Lecture Notes in Computer Science(), vol 12866. Springer, Cham. https://doi.org/10.1007/978-3-030-86324-1_21
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