Elsevier

Journal of Web Semantics

Volume 6, Issue 4, November 2008, Pages 266-273
Journal of Web Semantics

Revyu: Linking reviews and ratings into the Web of Data

https://doi.org/10.1016/j.websem.2008.09.003Get rights and content

Abstract

Revyu is a live, publicly accessible reviewing and rating Web site, designed to be usable by humans whilst transparently generating machine-readable RDF metadata for the Semantic Web, based on user input. The site uses Semantic Web 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.

Introduction

Reviews and ratings are widely available on the Web and are one major form of ‘user-generated content’ that has become associated with ‘Web 2.0’ [25]. However, despite the availability of reviews and ratings through APIs such as the Amazon Associates Web Service [2], this data remains largely isolated in ‘silos’, and described in formats that hinder its integration and interlinking with data from other sources. This presents considerable barriers to the aggregation of all reviews of a particular item from across the Web, as an item reviewed in one silo cannot easily be associated with the same item reviewed elsewhere. As has been recognised by previous authors [15], [16], the Semantic Web, or Web of Data, provides a technological platform with which to overcome this problem and Revyu is a significant and concrete step towards realising a solution.

Revyu is a live, publicly usable and well used reviewing and rating Web site, launched in November 2006 and available at http://revyu.com/. The site combines an approachable interface for the creation of reviews by human users with a range of APIs through which Semantic Web applications can access machine-readable data for reuse in third-party applications. The site has been developed using Semantic Web technologies and standards such as RDF [19] and SPARQL [26], and according to Linked Data principles [5] and best practices [7]. These features enable Revyu to readily consume data from external services for the creation of human-oriented ‘mashups’ while also seeding an ecosystem of interlinked review and rating data on the Web that is helping to bootstrap the Semantic Web as a whole. In the following sections we will describe Revyu in more detail, examine these human- and machine-oriented characteristics and discuss many of the underlying design decisions.

Section snippets

Revyu compared to conventional Web APIs

Revyu allows people to review and rate things simply by filling in a Web form. This style of interaction with the site will be familiar to those who have written reviews on sites such as Epinions [12], Amazon [1] or TripAdvisor [28]. Whilst this functionality is not especially novel, as a reviewing application Revyu improves significantly over other work in the area in a number of ways.

First, Revyu is not a data ‘silo’ that locks data away for ‘safe keeping’. Instead, reviews and ratings

A usable Semantic Web system

A major goal of Revyu was to create a Semantic Web application that could be used by non-specialist users, i.e. those with no experience or knowledge of Semantic Web technologies. This was achieved by making the creation and publication of RDF invisible to the reviewer, enabling users to contribute data to the Semantic Web through a familiar, Web 2.0-style mode of interaction. The Revyu home page is shown in Fig. 1.

As of May 2008 Revyu has attracted 837 reviews from 261 distinct reviewers. This

Revyu architecture and implementation

Revyu is built on the same technologies that support many conventional Web applications – Apache, MySQL and PHP – but is also fundamentally a Semantic Web application from the ground upwards. Backend storage of RDF triples is provided by a de-normalised MySQL database. The application layer uses the RDF API for PHP (RAP) [24] for accessing, querying, manipulating and serializing RDF data.

All site content – reviews, data about reviewers, data about reviewed items and the tags reviewers assign to

Linkable Data in Revyu

Rather than simply publishing ‘islands’ of unconnected RDF data on the Web, Revyu was designed from the outset to adhere to the four principles of Linked Data, outlined by Berners-Lee [5]: using URIs as names for things, using HTTP URIs so people can look up those names, providing useful information when someone looks up a URI, and linking to other URIs so more things can be discovered. By following these principles and Linked Data best practices [7] the site ensures that reviews it hosts can

Reusing existing personal profiles

External data about reviewers contributing to the site is also reused within Revyu. A common experience with existing Web applications is that, on registration, the user must create a new profile that duplicates profiles they have already created on other sites. This can increase the burden on the user as they must manage multiple redundant sets of personal information stored in different locations. In addition to consuming external data about items that have been reviewed on the site, Revyu

Inferring trust relationships from Revyu data

A broader goal of our research is to use knowledge held within trusted social networks to support information-seeking tasks on the Web. Data collected within Revyu forms a basis for this research, as the reviews created by users, and the tags they apply to items, enable us to infer trustworthiness relationships between people, with regard to particular topics. The algorithms used to generate metrics that represent these trust relationships are referred to as the ‘Hoonoh algorithms’.

These

Collecting and exposing social network data

In addition to consuming data from existing, external FOAF profiles, Revyu enables users to state that they know other Revyu reviewers. At this point the relationship is recorded in the triplestore using the foaf:knows property, and exposed (privacy settings permitting) in the user’s RDF description on the Revyu site. This ensures that social networking data created in Revyu is not automatically rendered inaccessible to other services, and can play a first class role in a broader, Web-wide

Future work and conclusions

In addition to encouraging further user participation in order to increase the value delivered by the site, we plan to integrate Revyu with a number of additional data sets, as discussed above. It should be noted that our aim in linking to external datasets is not to constrain, but merely to seed, users conceptions of what can be reviewed. As we integrate further data sets we hope to develop techniques for automated population of Revyu with data about reviewable items, from the Semantic Web at

Acknowledgements

This research was partially supported by the Advanced Knowledge Technologies (AKT) and OpenKnowledge (OK) projects. AKT is an Interdisciplinary Research Collaboration (IRC) sponsored by the UK Engineering and Physical Sciences Research Council under grant number GR/N15764/01. OK is sponsored by the European Commission as part of the Information Society Technologies (IST) programme under grant number IST-2001-34038. The Open Guides and DBpedia communities, and the RDF Book Mashup team deserve

References (31)

  • Amazon.com: http://www.amazon.com/ (accessed May 14,...
  • Amazon.com Associates Web Service: http://www.amazon.com/E-Commerce-Service-AWS-home-page/b?node=12738641 (accessed May...
  • S. Auer et al.

    DBpedia: a nucleus for a Web of Open Data

  • D. Ayers, T. Heath, Review Vocabulary, v0.2, http://purl.org/stuff/rev# (accessed May 14,...
  • T. Berners-Lee, Linked Data, http://www.w3.org/DesignIssues/LinkedData.html (accessed May 14,...
  • C. Bizer et al.

    The RDF Book Mashup: from Web APIs to a Web of Data

  • C. Bizer, R. Cyganiak, T. Heath, How to publish Linked Data on the Web,...
  • C. Bizer et al.

    Linked Data on the Web (LDOW2008)

  • D. Brickley, L. Miller, FOAF Vocabulary Specification 0.9, http://xmlns.com/foaf/0.1/ (accessed May 14,...
  • Creative Commons: http://creativecommons.org/ (accessed May 14,...
  • D. Eastlake, P. Jones, RFC 3174—US Secure Hash Algorithm 1 (SHA1), http://isc.faqs.org/rfcs/rfc3174.html (accessed May...
  • Epinions.com: http://www.epinions.com/ (accessed May 14,...
  • M. Gaved et al.

    Wikis of Locality: insights from the Open Guides

  • Geonames: http://geonames.org/ (accessed May 14,...
  • J. Golbeck et al.

    FilmTrust: movie recommendations using trust in Web-based social networks

  • Cited by (0)

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