Advertisement

Frontiers of Computer Science

, Volume 12, Issue 6, pp 1125–1139 | Cite as

Automatic Web-based relational data imputation

  • Hailong LiuEmail author
  • Zhanhuai Li
  • Qun Chen
  • Zhaoqiang Chen
Research Article
  • 14 Downloads

Abstract

Data incompleteness is one of the most important data quality problems in enterprise information systems. Most existing data imputing techniques just deduce approximate values for the incomplete attributes by means of some specific data quality rules or some mathematical methods. Unfortunately, approximation may be far away from the truth. Furthermore, when observed data is inadequate, they will not work well. The World Wide Web (WWW) has become the most important and the most widely used information source. Several current works have proven that using Web data can augment the quality of databases. In this paper, we propose a Web-based relational data imputing framework, which tries to automatically retrieve real values from the WWW for the incomplete attributes. In the paper, we try to take full advantage of relations among different kinds of objects based on the idea that the same kind of things must have the same kind of relations with their relatives in a specific world. Our proposed techniques consist of two automatic query formulation algorithms and one graph-based candidates extraction model. Several evaluations are proposed on two high-quality real datasets and one poor-quality real dataset to prove the effectiveness of our approaches.

Keywords

data incompleteness imputation World Wide Web query formulation candidate selection semantic relation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

The authors would like to thank the anonymous referees for their valuable comments and the recommendation of ICYCSEE 2016. The work was supported by the Ministry of Science and Technology of China, National Key Research and Development Program (2016YFB1000700), the National Natural Science Foundation of China (Grant Nos. 61502390, 61472321, 61402370, 61502392), and the Basic Research Fund of Northwestern Polytechnical University (3102015JSJ0004, 3102014JSJ0013, 3102014JSJ0005).

Supplementary material

11704_2016_6319_MOESM1_ESM.ppt (304 kb)
Supplementary material, approximately 304 KB.

References

  1. 1.
    Batista G E, Monard M C. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 2003, 17(5–6): 519–533CrossRefGoogle Scholar
  2. 2.
    Ramoni M, Sebastiani P. Robust learning with missing data. Machine Learning, 2001, 45(2): 147–170CrossRefzbMATHGoogle Scholar
  3. 3.
    Grzymala-Busse J W, Hu M. A comparison of several approaches to missing attribute values in data mining. In: Proceedings of the 2nd International Conference on Rough Sets and Current Trends in Computing. 2000, 378–385Google Scholar
  4. 4.
    Zhu X F, Zhang S C, Jin Z, Zhang Z L, Xu Z M. Missing value estimation for mixed-attribute data sets. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(1): 110–121CrossRefGoogle Scholar
  5. 5.
    Little R J, Rubin D B. Statistical Analysis with Missing Data. New York: John Wiley & Sons, 2002CrossRefzbMATHGoogle Scholar
  6. 6.
    Loshin D. Master Data Management. Boston: Morgan Kaufmann, 2010zbMATHGoogle Scholar
  7. 7.
    Schlaefer N, Ko J, Betteridge J, Sautter G, Pathak M A, Nyberg E. Semantic extensions of the Ephyra QA system for TREC 2007. In: Proceedings of the 16th Text REtrieval Conference. 2007, 332–341Google Scholar
  8. 8.
    Huhtala Y, Kärkkäinen J, Porkka P, Toivonen H. Tane: an efficient algorithm for discovering functional and approximate dependencies. The Computer Journal, 1999, 42(2): 100–111CrossRefzbMATHGoogle Scholar
  9. 9.
    Hollan J H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Cambridge, MA: MIT press, 1992CrossRefGoogle Scholar
  10. 10.
    Goldberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Pearson: Addison-Wesley Professional, 1989zbMATHGoogle Scholar
  11. 11.
    Li Z X, Sharaf MA, Sitbon L, Sadiq S, Indulska M, Zhou X F. Webput: efficient Web-based data imputation. In: Proceedings of the 13th International Conference on Web Information Systems Engineering. 2012, 243–256Google Scholar
  12. 12.
    Jurafsky D, James H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech. Upper Saddle River: Pearson Education, 2000Google Scholar
  13. 13.
    Finkel J R, Grenager T, Manning C. Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. 2005, 363–370Google Scholar
  14. 14.
    Fader A, Soderland S, Etzioni O. Identifying relations for open information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011, 1535–1545Google Scholar
  15. 15.
    Liu H L, Li Z H, Jin C Q, Chen Q. Web-based techniques for automatically detecting and correcting information errors in a database. In: Proceedings of the 3rd International Conference on Big Data and Smart Computing. 2016, 261–264Google Scholar
  16. 16.
    Lakshminarayan K, Harp S A, Goldman R, Samad T. Imputation of missing data using machine learning techniques. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. 1996, 140–145Google Scholar
  17. 17.
    Wang Q H, Rao J. Empirical likelihood-based inference in linear models with missing data. Scandinavian Journal of Statistics, 2002, 29(3): 563–576MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Zhang S C, Zhang J L, Zhu Z F, Qin Y S, Zhang C Q. Missing value imputation based on data clustering. Transactions on Computational Science, 2008, 128–138Google Scholar
  19. 19.
    Yakout M, Elmagarmid A K, Neville J, Ouzzani M, Ilyas I F. Guided data repair. Proceedings of the VLDB Endowment, 2011, 4(5): 279–289CrossRefGoogle Scholar
  20. 20.
    Tong Y X, Cao C C, Zhang C J, Li Y T and Chen L. Crowdcleaner: data cleaning for multi-version data on the Web via crowdsourcing. In: Proceedings of the 30th IEEE International Conference on Data Engineering. 2014, 1182–1185Google Scholar
  21. 21.
    Fan W F, Geerts F. Capturing missing tuples and missing values. In: Proceedings of the 29th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. 2010, 169–178Google Scholar
  22. 22.
    Fan W F, Geerts F. Relative information completeness. ACM Transactions on Database Systems, 2010, 35(4): 97–106CrossRefGoogle Scholar
  23. 23.
    Fan W F, Li J Z, Ma S, Tang N, Yu W Y. Towards certain fixes with editing rules and master data. Proceedings of the VLDB Endowment, 2010, 3(2): 213–238Google Scholar
  24. 24.
    Cirasella J. Google Sets, Google Suggest, and Google Search History: three more tools for the reference librarian’s bag of trick. The Reference Librarian, 2007, 48(1): 57–65CrossRefGoogle Scholar
  25. 25.
    Wang R C, Cohen W W. Language-independent set expansion of named entities using the Web. In: Proceedings of the 7th IEEE International Conference on Data Mining. 2007, 342–350Google Scholar
  26. 26.
    Wang R C, Cohen WW. Iterative set expansion of named entities using the Web. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008, 1091–1096Google Scholar
  27. 27.
    Sadamitsu K, Saito K, Imamura K, Kikui G. Entity set expansion using topic information. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 2011, 726–731Google Scholar
  28. 28.
    Dalvi B B, Cohen W W, Callan J. Websets: extracting sets of entities from the Web using unsupervised information extraction. In: Proceedings of the 5th ACM International Conference on Web search and Data Mining. 2012, 243–252Google Scholar
  29. 29.
    Bian H Q, Chen Y G, Du X Y, Zhang X L. MetKB: enriching RDF knowledge bases with Web entity-attribute tables. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. 2013, 2461–2464Google Scholar
  30. 30.
    Zhang X L, Chen Y G, Chen J C, Du X Y, Zou L. Mapping entity-attribute Web tables to web-scale knowledge bases. In: Proceedings of the 18th International Conference on Database Systems for Advanced Applications. 2013, 108–122CrossRefGoogle Scholar
  31. 31.
    Li Z X, Sharaf M A, Sitbon L, Du X Y, Zhou X F. CoRE: a context-aware relation extraction method for relation completion. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(4): 836–849CrossRefGoogle Scholar
  32. 32.
    Tang N, Vemuri V R. Web-based knowledge acquisition to impute missing values for classification. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence. 2004, 124–130CrossRefGoogle Scholar
  33. 33.
    Li Z X, Sharaf M A, Sitbon L, Sadiq S, Indulska M, Zhou X F. A web-based approach to data imputation. WorldWideWeb, 2014, 17(5): 873–897Google Scholar
  34. 34.
    Li Z X, Shang S, Xie Q, Zhang X L. Cost reduction for web-based data imputation. In: Proceedings of the 19th International Conference on Database Systems for Advanced Applications. 2014, 438–452CrossRefGoogle Scholar
  35. 35.
    Soderland S. Learning information extraction rules for semi-structured and free text. Machine Learning, 1999, 34(1–3): 233–272CrossRefzbMATHGoogle Scholar
  36. 36.
    Liu H L, Li Z H, Chen Q, Chen Z Q. A review on web-based techniques for automatically detecting and correcting information errors in relational databases. Chinese Journal of Computers, 2016, 40(10): 2286–2304Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Hailong Liu
    • 1
    Email author
  • Zhanhuai Li
    • 1
  • Qun Chen
    • 1
  • Zhaoqiang Chen
    • 1
  1. 1.School of Computer Science and TechnologyNorthwestern Polytechnical UniversityXi’anChina

Personalised recommendations