Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
World Wide Web
Published in: World Wide Web 2/2019

05-09-2018

A novel approach for Web page modeling in personal information extraction

Authors: Wei Yuliang, Zhou Qi, Lv Fang, Han Xixian, Xin Guodong, Wang Bailing

Published in: World Wide Web | Issue 2/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The target of personal information extraction (PIE) is to extract content associated with a name form Web pages. Available Web page models, which are also used widely in content extraction and automatic wrapper algorithms, include text model, document object model, and vision-based page segmentation model. Because of existing models focus on Web structure rather than semantic relevance, they are difficult to be directly used for PIE. To deal with this problem, we introduce the sequence block model (SBM), by which is easy to determine the relevance of each page block to the retrieval name. Then, we give the definition of PIE based on the SBM. Depending on the sequence correlation of SBM, we design a 4-layer seq2seq deep learning network for PIE. Experiment result shows that our new model extracts twice as much data as content extraction algorithms. And the recall rate of the network is 7% higher than the traditional model with classification algorithm.
previous article Deep learning approaches for video-based anomalous activity detection
next article Residual attention-based LSTM for video captioning

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Banu, A., Chitra, M.: Dwde-ir: an efficient deep Web data extraction for information retrieval on Web mining. J. Emerg. Technol. Web Intell. 6(1), 133–141 (2014)
2.
Bartoli, A., De Lorenzo, A., Medvet, E., Tarlao, F.: Inference of regular expressions for text extraction from examples. IEEE Trans. Knowl. Data Eng. 28(5), 1217–1230 (2016)CrossRef
3.
Bu, Z., Zhang, C., Xia, Z., Wang, J.: An far-sw based approach for Webpage information extraction. Inf. Syst. Front. 16(5), 771–785 (2014)CrossRef
4.
Cai, D., Yu, S., Wen, J.R., Ma, W.Y.: VIPS: vision-based page segmentation algorithm. Microsoft Technical Report, MSR-TR-2003-79 (2003)
5.
Chang, C.H., Kayed, M., Girgis, M.R., Shaalan, K.F.: A survey of Web information extraction systems. IEEE Trans. Knowl. Data Eng. 18(10), 1411–1428 (2006)CrossRef
6.
7.
8.
Dalvi, N., Kumar, R., Soliman, M.: Automatic wrappers for large scale Web extraction. Proc. VLDB Endow. 4(4), 219–230 (2011)CrossRef
9.
Doddington, G.R., Mitchell, A., Przybocki, M.A., Ramshaw, L.A., Strassel, S., Weischedel, R.M.: The automatic content extraction (ace) program-tasks, data, and evaluation. In: LREC, vol. 2, pp. 837–840 (2004)
10.
Ferrara, E., De Meo, P., Fiumara, G., Baumgartner, R.: Web data extraction, applications and techniques: a survey. Knowl.-Based Syst. 70, 301–323 (2014)CrossRef
11.
Flesca, S., Manco, G., Masciari, E., Rende, E., Tagarelli, A.: Web wrapper induction: a brief survey. AI Commun. 17(2), 57–61 (2004)
12.
Furche, T., Gottlob, G., Grasso, G., Schallhart, C., Sellers, A.: Oxpath: a language for scalable data extraction, automation, and crawling on the deep Web. VLDB J. 22(1), 47–72 (2013)CrossRef
13.
Gao, L., Guo, Z., Zhang, H., Xu, X., Shen, H.T.: Video captioning with attention-based lstm and semantic consistency. IEEE Trans. Multimedia 19(9), 2045–2055 (2017)CrossRef
14.
Gogar, T., Hubacek, O., Sedivy, J.: Deep neural networks for Web page information extraction. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 154–163. Springer, Berlin (2016)
15.
Grigalis, T., Radvilavičius, L., Čenys, A., Gordevičius, J.: Clustering visually similar Web page elements for structured Web data extraction. In: Web Engineering, pp. 435–438 (2012)
16.
Hadnagy, C.: Social Engineering: the Art of Human Hacking. Wiley, New York (2010)
17.
18.
19.
20.
Krishna, S.S., Dattatraya, J.S.: Schema inference and data extraction from templatized Web pages. In: 2015 International Conference on Pervasive Computing (ICPC), pp. 1–6. IEEE (2015)
21.
Kushmerick, N.: Finite-state approaches to Web information extraction. In: Lecture Notes in Computer Science, pp. 77–91 (2003)
22.
Li, Y., Krishnamurthy, R., Raghavan, S., Vaithyanathan, S., Jagadish, H.: Regular expression learning for information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 21–30. Association for Computational Linguistics (2008)
23.
Li, J.Q., Zhao, Y., Garcia-Molina, H.: A path-based approach for Web page retrieval. World Wide Web 15(3), 257–283 (2012)CrossRef
24.
Mann, G.S., Yarowsky, D.: Unsupervised personal name disambiguation. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003-Volume 4, pp. 33–40. Association for Computational Linguistics (2003)
25.
Saleh, A.I., Al Rahmawy, M.F., Abulwafa, A.E.: A semantic based Web page classification strategy using multi-layered domain ontology. World Wide Web 20(5), 939–993 (2017)CrossRef
26.
Sanoja, A., Gancarski, S.: Block-o-matic: a Web page segmentation framework. In: 2014 International Conference on Multimedia Computing and Systems (ICMCS), pp. 595–600. IEEE (2014)
27.
Sleiman, H.A., Corchuelo, R.: Tex: an efficient and effective unsupervised Web information extractor. Knowl.-Based Syst. 39, 109–123 (2013)CrossRef
28.
Song, D., Sun, F., Liao, L.: A hybrid approach for content extraction with text density and visual importance of dom nodes. Knowl. Inf. Syst. 42(1), 75–96 (2015)CrossRef
29.
Song, J., Zhang, H., Li, X., Gao, L., Wang, M., Hong, R.: Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans. Image Process. 27 (7), 3210–3221 (2018)MathSciNetCrossRefMATH
30.
Thamviset, W., Wongthanavasu, S.: Information extraction for deep Web using repetitive subject pattern. World Wide Web 17(5), 1109–1139 (2014)CrossRef
31.
Vijendran, A.S., Deepa, C.: LBDA: a novel framework for extracting content from Web pages. In: 2013 International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 1–7. IEEE (2013)
32.
Wei, Y., Wang, B., Liu, Y., Lv, F.: Research on Webpage similarity computing technology based on visual blocks. In: Chinese National Conference on Social Media Processing, pp. 187–197 (2014)
33.
Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)CrossRef
34.
Wu, G., Li, L., Hu, X., Wu, X.: Web news extraction via path ratios. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2059–2068. ACM (2013)
35.
Xu, X., Shen, F., Yang, Y., Shen, H.T., Li, X.: Learning discriminative binary codes for large-scale cross-modal retrieval. IEEE Trans. Image Process. 26(5), 2494–2507 (2017)MathSciNetCrossRefMATH
36.
Xu, X., He, L., Lu, H., Gao, L., Ji, Y.: Deep adversarial metric learning for cross-modal retrieval. World Wide Web, pp. 1–16 (2018)
37.
Zhang, C., Liu, C., Zhang, X., Almpanidis, G.: An up-to-date comparison of state-of-the-art classification algorithms. Expert. Syst. Appl. 82, 128–150 (2017)CrossRef
Metadata
Title
A novel approach for Web page modeling in personal information extraction
Authors
Wei Yuliang
Zhou Qi
Lv Fang
Han Xixian
Xin Guodong
Wang Bailing
Publication date
05-09-2018
Publisher
Springer US
Published in
World Wide Web / Issue 2/2019
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-018-0631-9

Other articles of this Issue 2/2019

World Wide Web 2/2019 Go to the issue

OriginalPaper

Generalized zero-shot learning for action recognition with web-scale video data

OriginalPaper

An emotion-based responding model for natural language conversation

OriginalPaper

Context-aware graph pattern based top-k designated nodes finding in social graphs

OriginalPaper

Semi-supervised cross-modal learning for cross modal retrieval and image annotation

OriginalPaper

Residual attention-based LSTM for video captioning

OriginalPaper

Deep learning approaches for video-based anomalous activity detection

Premium Partner

    Image Credits