Abstract
Web users these days are confronted with an abundance of information. While this is clearly beneficial in general, there is a risk of “information overload”. To this end, there is an increasing need of filtering, classifying and/or summarizing Web contents automatically. In order to help consumers in efficiently deriving the semantics from Web contents, we have developed the CALVADOS (Content AnaLytics ViA Digestion Of Semantics) system. To this end, CALVADOS raises contents to the entity-level and digests its inherent semantics. In this demo, we present how entity-level analytics can be employed to automatically classify the main topic of a Web content and reveal the semantic building blocks associated with the corresponding document.
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Acknowledgements
This work was supported by the RIN RECHERCHE Normandie Digitale research project ASTURIAS contract no. 18E01661. We thank our colleagues for inspiring discussions.
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Govind, Kumar, A., Alec, C., Spaniol, M. (2019). CALVADOS: A Tool for the Semantic Analysis and Digestion of Web Contents. In: Hitzler, P., et al. The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science(), vol 11762. Springer, Cham. https://doi.org/10.1007/978-3-030-32327-1_17
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DOI: https://doi.org/10.1007/978-3-030-32327-1_17
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