Inferring Web Citations using Social Data and SPARQL Rules

Web users who disseminate their personal information risk falling victims to malevolent web practices such as lateral surveillance and identity theft. To avoid such practices, web users must manually search for web resources which may cite them and then perform analyses to decide which resources do cite them; a time-consuming and frequently repeated practice. This paper presents a automated rule-based technique to identify web citations, intended to alleviate this manual process. Seed data is leveraged from user profiles on various Social Web platforms and is then used as seed data from which SPARQL rules are constructed and applied in infer web citations. An evaluation of this technique against humans performing the same task shows higher levels of precision.