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This paper uses Twitter as a microblogging platform to link hashtags, which relate the message to a topic that is shared among users, to Wikidata, a central knowledge base of information relying on its members and machine bots to keeping its content up to date. The data is stored in a highly structured format, with the added SPARQL Protocol And RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base. Our research, designs and implements a process to stream live Twitter tweets and to parse existing Wikidata revision XML files provided by Wikidata. Furthermore, we identify if a correlation exists between the top Twitter hashtags and Wikidata revisions over a seventy-seven-day period.We have used statistical evaluation tools, such as ‘Jaccard Ratio’ and ‘Kolmogorov-Smirnov’ to investigate a significant statistical correlation between Twitter hashtags and Wikidata revisions over the studied period.
Paula Dooley and Bojan Božić. 2019. Towards Linked Data for Wikidata Revisions and Twitter Trending Hashtags. In The 21st International Conference on Information Integration and Web-based Applications Services (iiWAS2019), December 2–4, 2019, Munich, Germany. ACM, New York, NY, USA, 10 pages. DOI: 10.1145/3366030.3366048