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Assessing Web Article Quality by Harnessing Collective Intelligence

  • Conference paper
Database Systems for Advanced Applications (DASFAA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7238))

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Abstract

Existing approaches assess web article’s quality mainly based on syntax, but seldom work is given on how to quantify its quality based on semantics. In this paper we propose a novel Semantic Quality Assessment(SQA) approach to automatically determine data quality in terms of two most important quality dimensions, namely accuracy and completeness. First, alternative context with respect to source article is built by collecting alternative web articles. Second, each alternative article is transformed and represented by semantic corpus and dimension baselines are synthetically generated from these semantic corpora. Finally, quality dimension of source article is determined by comparing its semantic corpus with dimension baseline. Our approach is promising way to assess web article quality by exploiting available collective knowledge. Experiments show that our approach performs well.

The work is supported by National Natural Science Foundation of China under grants No. 61003040, 61100135, 60970030, 60903181 and 40801149.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210003, P.R.China

    Jingyu Han, Xueping Chen & Kejia Chen

  2. School of Computing, National University of Singapore, Singapore, 119077

    Dawei Jiang

Authors
  1. Jingyu Han
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  2. Xueping Chen
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  3. Kejia Chen
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  4. Dawei Jiang
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, 151747, Seoul, South Korea

    Sang-goo Lee

  2. Computer School, Wuhan University, Luo-jia-shan, Wuchang, 430081, Wuhan, Hubei Province, China

    Zhiyong Peng

  3. School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Brisbane, Australia

    Xiaofang Zhou

  4. Department of Computer Science, Kangwon National University, 192-1, Hyoja2-Dong, Chuncheon, 200701, Kangwon, South Korea

    Yang-Sae Moon

  5. Institute for Computer Science and Business Information, University of Duisburg-Essen, SchĂĽtzenbahn 70, 45117, Essen, Germany

    Rainer Unland

  6. School of Information and Communication Engineering, Chungbuk National University, 52 Naesudong-ro, Heungdeok-gu, Cheongju, 4072, Chungbuk, South Korea

    Jaesoo Yoo

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© 2012 Springer-Verlag Berlin Heidelberg

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Han, J., Chen, X., Chen, K., Jiang, D. (2012). Assessing Web Article Quality by Harnessing Collective Intelligence. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_31

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  • DOI: https://doi.org/10.1007/978-3-642-29038-1_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29037-4

  • Online ISBN: 978-3-642-29038-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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