Revyu: Linking Reviews and Ratings into the Web of Data

Abstract

Revyu is a live, publicly accessible reviewing and ratingWeb site, designed to be usable by humans whilst transparently generating machine-readable RDF metadata for the SemanticWeb, based on user input. The site uses SemanticWeb specifications such as RDF and SPARQL, and the latest Linked Data best practices to create a major node in a potentially Web-wide ecosystem of reviews and related data. Throughout the implementation of Revyu design decisions have been made that aim to minimize the burden on users, by maximizing the reuse of external data sources, and allowing less structured human input (in the form of Web 2.0-style tagging) from which stronger semantics can later be derived. Links to external sources such as DBpedia are exploited to create human-oriented mashups at the HTML level, whilst links are also made in RDF to ensure Revyu plays a first class role in the blossoming Web of Data. In this paper we document design decisions made during the implementation of Revyu, discuss the techniques used for linking Revyu data with external sources, and outline how data from the site is being used to infer the trustworthiness of reviewers as sources of information and recommendations.