article

Automating user reviews using ontologies: an agent-based approach

Authors:

Pınar YolumAuthors Info & Claims

World Wide Web, Volume 15, Issue 3

Pages 285 - 323

https://doi.org/10.1007/s11280-011-0134-4

Published: 01 May 2012 Publication History

Abstract

The Web is becoming a global market place, where the same services and products are offered by different providers. When obtaining a service, consumers have to select one provider among many alternatives to receive a service or buy a product. In real life, when obtaining a service, many consumers depend on the user reviews. User reviews--presumably written by other consumers--provide details on the consumers' experiences and thus are more informative than ratings. The down side is that such user reviews are written in natural language, making it extremely difficult to be interpreted by computers. Therefore, current technologies do not allow automation of user reviews and require too much human effort for tasks such as writing and reading reviews for the providers, aggregating existing information, and finally choosing among the possible candidates. In this paper, we represent consumers' reviews as machine processable structures using ontologies and develop a layered multiagent framework to enable consumers to find satisfactory service providers for their needs automatically. The framework can still function successfully when consumers evolve their language and when deceptive reviewers enter the system. We show the flexibility of the framework by employing different algorithms for various tasks and evaluate them for different circumstances.

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Cited By

Bhama SKarpagam G(2019)Application of multivariate Gaussian model for discovery of healthcare services in cloudCluster Computing10.1007/s10586-018-1935-322:2(3087-3094)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10586-018-1935-3
Fazzolari MPetrocchi MSpognardi A(2018)Predicting Online Review Scores Across Reviewer CategoriesIntelligent Data Engineering and Automated Learning – IDEAL 201810.1007/978-3-030-03493-1_73(698-710)Online publication date: 21-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-03493-1_73
Si JLi QQian TDeng X(2014)Users' interest grouping from online reviews based on topic frequency and orderWorld Wide Web10.1007/s11280-013-0239-z17:6(1321-1342)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1007/s11280-013-0239-z

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cover image World Wide Web

World Wide Web Volume 15, Issue 3

May 2012

146 pages

ISSN:1386-145X

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Copyright © Copyright © 2012 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 May 2012

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Cited By

Bhama SKarpagam G(2019)Application of multivariate Gaussian model for discovery of healthcare services in cloudCluster Computing10.1007/s10586-018-1935-322:2(3087-3094)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10586-018-1935-3
Fazzolari MPetrocchi MSpognardi A(2018)Predicting Online Review Scores Across Reviewer CategoriesIntelligent Data Engineering and Automated Learning – IDEAL 201810.1007/978-3-030-03493-1_73(698-710)Online publication date: 21-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-03493-1_73
Si JLi QQian TDeng X(2014)Users' interest grouping from online reviews based on topic frequency and orderWorld Wide Web10.1007/s11280-013-0239-z17:6(1321-1342)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1007/s11280-013-0239-z

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