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Web article quality ranking based on web community knowledge

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Abstract

The Web article has been recognized as the most popular data source for its convenience and abundance of information. Yet its data quality is compromised as most of existing quality assessment approaches rely mainly on the syntax or lexicon, rather than the semantics. We propose a Fact-based Quality Assessment (FQA) approach, which captures the data quality based on content semantics by gleaning the Web community knowledge. The FQA can automatically rank the Web data quality in terms of the three most important quality dimensions accuracy, completeness and freshness. Furthermore, the semantic dimensions can well complement existing works based on syntactical or lexical features. Given one source article, the FQA starts with the identification of an alternative context by collecting articles of the same topics. Then, the dimension baselines of accuracy, completeness and freshness are extracted in the alternative context. Finally, the data quality is determined by comparing the semantic corpus of the source article with the established dimension baselines. The performance of our FQA is verified in the experiments.

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Notes

  1. http://www.bbc.co.uk/history/historic_figures/darwin_charles.shtml.

  2. http://www.lucidcafe.com/library/96feb/darwin.html.

  3. http://nlp.stanford.edu/software/dcoref.shtml.

  4. http://reverb.cs.washington.edu/.

  5. http://wordnet.princeton.edu/.

  6. http://en.wikipedia.org/wiki/Scientist.

  7. http://en.wikipedia.org/wiki/Wikipedia:Version_1.0_Editorial_Team/Assessment.

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Acknowledgments

We sincerely thank Professor Alexandra Poulovassilis from London Knowledge Lab(LKL) for her valuable suggestions, and the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Nanjing University of Posts and Telecommunications, P.O.X 139, Nanjing, 210003, People’s Republic of China

    Jingyu Han & Kejia Chen

  2. Department of Computer Science and Information Systems, Birkbeck College, London, UK

    Jianing Wang

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  1. Jingyu Han
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  2. Kejia Chen
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  3. Jianing Wang
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Correspondence to Jingyu Han.

Additional information

This research is fully supported by National Natural Science Foundation of China under the grant numbers 61003040, 61100135.

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Han, J., Chen, K. & Wang, J. Web article quality ranking based on web community knowledge. Computing 97, 509–537 (2015). https://doi.org/10.1007/s00607-014-0435-4

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