Abstract
Extracting information from the Web is one of the most trending subjects for data analysts and scientists. Web scraping is one of the prominent means to do so by parsing HTML pages and extracting data from their embedded tags. To that end, software developers write customized scripts for each target Website. Crafting such scripts is a challenging task due to the different structures of Web sites and their dynamic rendering. Yet, a more challenging task is to infer semantic annotations from a news article. It is interesting, for example, to have a word such as the US in an already published article to be automatically annotated semantically by its population and area. We argue that these annotations should not be mere hyperlinks to be consumed by human readers but rather they can be machine readable using the Web standard RDF (Resource Description Framework). Embedding such RDF inside the HTML page of the news article should enrich it with automatically generated semantics that can boost its SEO (Search Engine Optimization) and are ready to be consumed by conventional Web scrapers. As a proof of concept, we built a prototype that focuses on the plain text of an article, without stipulating the existence of structured tags and attributes, to generate a new Web document that is augmented with semantic annotations using the RDF markup language, readable by humans and machine-consumable by Web scrapers.
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Salem, H., Mazzara, M., Elnaffar, S. (2021). Automatically Injecting Semantic Annotations into Online Articles. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-030-75078-7_61
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DOI: https://doi.org/10.1007/978-3-030-75078-7_61
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