Advertisement

A semi-structured information semantic annotation method for Web pages

  • Lu Zhang
  • Tiantian Wang
  • Yiran Liu
  • Qingling Duan
Multi-Source Data Understanding (MSDU)
  • 9 Downloads

Abstract

There is a large amount of semi-structured information on Web pages. Comprehensive and accurate annotation of Web page information with uniform semantics can enhance the use value of information and provide support for Web site information integration. According to the characteristics of semi-structured information on Web pages, a semantic annotation method based on header recognition and data item classification is proposed. Firstly, a description model is constructed for the domain to be annotated. Secondly, header recognition is used to annotate data items on extracted pages. For those data items fail to be annotated by header recognition, feature vectors are constructed based on the feature sets in the domain description model and semantics of those data items are annotated by the classification results of back-propagation neural network. The proposed method is tested on 19,657 data items in the domain of agricultural product price and 8089 data items in the domain of recruitment information. The annotation precision is 97.39% and 95.67% respectively, and the annotation recall is 95.41% and 95.67%, respectively. These results show that the proposed method can annotate semi-structured information on Web pages accurately and completely.

Keywords

Semantic annotation Semi-structured information Back-propagation neural network Domain description model 

Notes

Acknowledgements

This research was supported by the Key Research and Development Program of Shandong Province—“Research and Demonstration on Accurate Monitoring and Control Technology of Facilities Vegetable Environment” (Grant No. 2017CXGC0201), the Transformation and Popularization Project of Agricultural Scientific and Technological Achievements in Tianjin—“Integrated Application of Core Information Technology for Early Warning, Diagnosis and Prevention of Greenhouse Vegetable Diseases” (Grant No. 201704070) and the “12th Five-Year” National Science and Technology Support Plan Project (Grant No. 2012BAD35B06).

References

  1. 1.
    Zhou P, El-Gohary N (2017) Ontology-based automated information extraction from building energy conservation codes. Autom Constr.  https://doi.org/10.1016/j.autcon.2016.09.004 Google Scholar
  2. 2.
    Kim J, Vasardani M, Winter S (2017) Similarity matching for integrating spatial information extracted from place descriptions. Int J Geogr Inf Syst.  https://doi.org/10.1080/13658816.2016.1188930 Google Scholar
  3. 3.
    Varlamov MI, Turdakov D (2016) A survey of methods for the extraction of information from Web resources. Program Comput Softw 42(5):279–291.  https://doi.org/10.1080/13658816.2016.1188930 CrossRefGoogle Scholar
  4. 4.
    Wei Y, Zhang G, Chang Y et al (2009) Deep web semantic annotation method based on chinese part-of-speech and domain knowledge. J Zhengzhou Univ (Nat Sci Ed) 41(01):52–55Google Scholar
  5. 5.
    Li G, Chin B, Jianhua O, et al (2008) Ease: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. In: Acm Sigmod international conference on management of data. ACM.  https://doi.org/10.1145/1376616.1376706
  6. 6.
    Abiteboul S (1997) Querying semi-structured data. In: International conference on database theory. Springer.  https://doi.org/10.1007/3-540-62222-5_33
  7. 7.
    Guezoulia L, Essafibc H (2016) CAS-based information retrieval in semi-structured documents: CASISS model. J Innov Digit Ecosyst.  https://doi.org/10.1016/j.jides.2016.11.004 Google Scholar
  8. 8.
    Al-Yahya M, Al-Shaman M, Al-Otaiby N et al (2015) Ontology-based semantic annotation of Arabic language text. Int J Mod Educ Comput Sci 7(7):53–59.  https://doi.org/10.5815/ijmecs.2015.07.07 CrossRefGoogle Scholar
  9. 9.
    Albukhitan S, Alnazer A, Helmy T (2016) Semantic annotation of Arabic web resources using semantic web services. Procedia Comput Sci 83:504–511.  https://doi.org/10.1016/j.procs.2016.04.243 CrossRefGoogle Scholar
  10. 10.
    Rajput Q, Haider S (2011) BNOSA: A Bayesian network and ontology based semantic annotation framework. Web Semant Sci Serv Agents World Wide Web 9(2):99–112.  https://doi.org/10.1016/j.websem.2011.04.002 CrossRefGoogle Scholar
  11. 11.
    Yuan L, Li Z, Chen S (2008) Online-based deep web data annotation. J Softw 19(2):237–245.  https://doi.org/10.3724/sp.j.1001.2008.00237 CrossRefGoogle Scholar
  12. 12.
    Zhu X (2012) Research on key issues of deep web semantic annotation based on ontology learning. Soochow University.  https://doi.org/10.7666/d.y2121209
  13. 13.
    Chen Y, Li W, Peng X et al (2009) Improved semantic annotation method for documents based on ontology. J Southeast Univ 39(6):1109–1113.  https://doi.org/10.3969/j.issn.1001-0505.2009.06.005 Google Scholar
  14. 14.
    Li M, Li X (2011) Deep Web data annotation method based on result schema. J Comput Appl 31(7):1733–1736.  https://doi.org/10.3724/SP.J.1087.2011.01733 Google Scholar
  15. 15.
    Li X (2011) Deep web data annotation based on result schema. Lanzhou University of Technology.  https://doi.org/10.7666/d.y1885776
  16. 16.
    Ma A, Gao K, Zhang X et al (2009) Semantic annotation based on CPN network for Deep Web data. J Northeastern Univ 30(6):794–797.  https://doi.org/10.3321/j.issn:1005-3026.2009 Google Scholar
  17. 17.
    Dong Y, Li Q, Ding Y, Peng Z (2012) Web data semantic annotation based on constraint conditional random fields. J Comput Res Dev 49(02):361–371Google Scholar
  18. 18.
    Dill S, Eiron N, Gibson D et al (2004) A case for automated large-scale semantic annotation. Web Semant Sci Serv Agents World Wide Web 1(1):115–132.  https://doi.org/10.1016/j.websem.2003.07.006 CrossRefGoogle Scholar
  19. 19.
    Dugas M, Meidt A, Neuhaus P et al (2016) ODMedit: uniform semantic annotation for data integration in medicine based on a public metadata repository. BMC Med Res Methodol 16(1):65.  https://doi.org/10.1186/s12874-016-0164-9 CrossRefGoogle Scholar
  20. 20.
    Vargasvera M, Motta E, Domingue J et al (2002) MnM: ontology driven semi-automatic and automatic support for semantic markup. In: International conference on knowledge engineering and knowledge management ontologies and the semantic web. Springer.  https://doi.org/10.1007/3-540-45810-7_34
  21. 21.
    Ji S (2017) Research on key technologies of multi-source information integration for joint operations. Hangzhou Dianzi UniversityGoogle Scholar
  22. 22.
    Amanqui FKM, Verborgh R, Mannens E et al (2016) Using spatiotemporal information to integrate heterogeneous biodiversity semantic data. In: International conference on web engineering. Springer.  https://doi.org/10.1007/978-3-319-38791-8_41
  23. 23.
    Zhu X (2012) Research on key issues of deep web semantic annotation based on ontology learning. Suzhou University.  https://doi.org/10.7666/d.y2121209
  24. 24.
    Pech F, Martinez A, Estrada H et al (2017) Semantic annotation of unstructured documents using concepts similarity. Sci Program 2017(2):1–10.  https://doi.org/10.1155/2017/7831897 Google Scholar
  25. 25.
    Yao X, Han J, Cheng G et al (2016) Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Trans Geosci Remote Sens 54(6):3660–3671.  https://doi.org/10.1109/TGRS.2016.2523563 CrossRefGoogle Scholar
  26. 26.
    Azar ER (2017) Semantic annotation of videos from equipment-intensive construction operations by shot recognition and probabilistic reasoning. J Comput Civ Eng.  https://doi.org/10.1061/(asce)cp.1943-5487.0000693 Google Scholar
  27. 27.
    Li G, Duan Q, Li D et al (2013) Chinese deep web query interfaces scheme matching based on AHPH. Comput Eng Des 34(1):293–297.  https://doi.org/10.3969/j.issn.1000-7024.2013.01.055 MathSciNetGoogle Scholar
  28. 28.
    Huang Y (2013) Research on application of BP neural network in data classification of information system. China University of Geosciences (Beijing)Google Scholar
  29. 29.
    Kumar S, Kumar K, Pandey AK (2016) Dynamic channel allocation in mobile multimedia networks using error back propagation and hopfield neural network (EBP-HOP). Procedia Comput Sci 89:107–116.  https://doi.org/10.1016/j.procs.2016.06.015 CrossRefGoogle Scholar
  30. 30.
    Erguzel TT, Ozekes S, Tan O et al (2015) Feature selection and classification of electroencephalographic signals: an artificial neural network and genetic algorithm based approach. Clin EEG Neurosci 46(4):321.  https://doi.org/10.1177/1550059414523764 CrossRefGoogle Scholar
  31. 31.
    Mohamed B, Issam A, Mohamed A et al (2015) ECG image classification in real time based on the haar-like features and artificial neural networks. In: International conference on advanced wireless information and communication technologies, pp 32–39.  https://doi.org/10.1016/j.procs.2015.12.045
  32. 32.
    Nawi NM, Khan A, Chiroma H et al (2014) Weight optimization in recurrent neural networks with hybrid metaheuristic cuckoo search techniques for data classification. Math Probl Eng 2015(4):1–12.  https://doi.org/10.1155/2015/868375 Google Scholar
  33. 33.
    Zhu X, Zhang S, He W, Hu R, Lei C, Zhu P (2018) One-step multi-view spectral clustering. IEEE Trans Knowl Data Eng.  https://doi.org/10.1109/tkde.2018.2873378 Google Scholar
  34. 34.
    Zhu X, Zhang S, Li Y, Zhang J, Yang L, Fang Y (2018) Low-rank sparse subspace for spectral clustering. IEEE Trans Knowl Data Eng.  https://doi.org/10.1109/tkde.2018.2858782 Google Scholar
  35. 35.
    MA Anxiang (2009) A research on key technology of deep web data integration based on result pattern. Northeastern University.. https://doi.org/10.7666/d.y1717244
  36. 36.
    Zheng W, Zhu X, Wen G, Zhu Y, Yu H, Gan J (2018) Unsupervised feature selection by self-paced learning regularization. Pattern Recognit Lett.  https://doi.org/10.1016/j.patrec.2018.06.029 Google Scholar
  37. 37.
    Zhu X, Zhang S, Hu R, Zhu Y, Song J (2018) Local and global structure preservation for robust unsupervised spectral feature selection. IEEE Trans Knowl Data Eng 30(3):517–529.  https://doi.org/10.1109/TKDE.2017.2763618 CrossRefGoogle Scholar
  38. 38.
    Zheng W, Zhu X, Zhu Y, Hu R, Lei C (2018) Dynamic graph learning for spectral feature selection. Multimedia Tools Appl 77(22):29739–29755.  https://doi.org/10.1007/s11042-017-5272-y CrossRefGoogle Scholar
  39. 39.
    Platt JC (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. In: Advances in kernel methods-support vector learning. pp 212–223.  https://doi.org/10.3390/s16091462
  40. 40.
    Friedman N, Geiger D, Idt MG (1997) Bayesian network classifiers. Mach Learn 29:131–163.  https://doi.org/10.1023/A:1007465528199 CrossRefzbMATHGoogle Scholar
  41. 41.
    Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11(1):63–90.  https://doi.org/10.1023/A:1022631118932 MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Lu Zhang
    • 1
  • Tiantian Wang
    • 1
  • Yiran Liu
    • 1
  • Qingling Duan
    • 1
  1. 1.College of Information and Electrical EngineeringChina Agricultural UniversityBeijingChina

Personalised recommendations