How to create persistent, machine- and human-readable citations of Web rankings

Data citation is a practice that has been a concern for many years and in the last decades has gained a growing interest due to the increasing importance of data in scholarly communication and business field. The practice of citing data aims to give credit and attribution acknowledging the contributions of data creators, collaborators, scientists, and institutions, while also ensuring the reproducibility of data by guaranteeing long-term access to these resources. In this context, an emerging topic revolves around the overlooked domain of reproducible web rankings, a branch of information retrieval that focuses on capturing information related to searches conducted on search engines and systems, and generating citations based on this data. This topic finds applications in various domains, including scientific research and decision-making processes. This thesis introduces the "ranking citation" model developed and made available in the form of a Chrome extension named "Unipd Ranking Citation Tool." It is constructed based on technologies from web development such as RDF graphs, ontologies, JSON languages, and Research Object, adhering to Linked Open Data and FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The tool provided to users is capable of meeting the requirements necessary for reproducing web rankings, offering an automated method to produce both human- and machine-readable resources.