Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Web Information Extraction

  • Laura Chiticariu
  • Marina Danilevsky
  • Howard Ho
  • Rajasekar Krishnamurthy
  • Yunyao Li
  • Sriram Raghavan
  • Frederick Reiss
  • Shivakumar Vaithyanathan
  • Huaiyu Zhu
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_459

Synonyms

Information extraction; Text analytics

Definition

Information extraction (IE) is the process of automatically extracting structured pieces of information from unstructured or semi-structured text documents. Classical problems in information extraction include named-entity recognition (identifying mentions of persons, places, organizations, etc.) and relationship extraction (identifying mentions of relationships between such named entities). Web information extraction is the application of IE techniques to process the vast amounts of unstructured content on the Web. Due to the nature of the content on the Web, in addition to named-entity and relationship extraction, there is growing interest in more complex tasks such as extraction of reviews, opinions, and sentiments.

Historical Background

Historically, information extraction was studied by the Natural Language Processing community in the context of identifying organizations, locations, and person names in news articles and...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Akbik A, Konomi O, Melnikov M. Propminer: a workflow for interactive information extraction and exploration using dependency trees. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics; 2013.Google Scholar
  2. 2.
    Appelt DE, Onyshkevych B. The common pattern specification language. In: Proceedings of the TIPSTER Text Program: Phase III. 1998.Google Scholar
  3. 3.
    Atasu K, Polig R, Hagleitner C, Reiss FR. Hardware-accelerated regular expression matching for high-throughput text analytics. In: Proceedings of the 23rd International Conference on Field programmable Logic and Applications; 2013. p. 1–7.Google Scholar
  4. 4.
    Boguraev B. Annotation-based finite state processing in a large-scale NLP architecture. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing. 2003.Google Scholar
  5. 5.
    Bohannon P, Merugu S, Yu C, Agarwal V, DeRose P, Iyer AS, Jain A, Kakade V, Muralidharan M, Ramakrishnan R, Shen W. Purple sox extraction management system. SIGMOD Rec. 2008;37(4):21–27.CrossRefGoogle Scholar
  6. 6.
    Brauer F, Rieger R, Mocan A, Barczynski WM. Enabling information extraction by inference of regular expressions from sample entities. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management; 2011.Google Scholar
  7. 7.
    Burdick D, Hernández M, Ho H, Koutrika G, Krishnamurthy R, Popa L, Stanoi IR, Vaithyanathan S, Das S. Extracting, linking and integrating data from public sources: a financial case study. IEEE Data Eng Bull. 2011;34(3):60–67.Google Scholar
  8. 8.
    Cafarella MJ, Etzion O. A search engine for natural language applications. In: Proceedings of the 14th International World Wide Web Conference; 2005.Google Scholar
  9. 9.
    Chiticariu L, Krishnamurthy R, Li Y, Raghavan S, Reiss F, Vaithyanathan S. Systemt: an algebraic approach to declarative information extraction. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics; 2010.Google Scholar
  10. 10.
    Chiticariu L, Krishnamurthy R, Li Y, Reiss F, Vaithyanathan S. Domain adaptation of rule-based annotators for named-entity recognition tasks. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing; 2010.Google Scholar
  11. 11.
    Chiticariu L, Li Y, Reiss FR. Rule-based information extraction is dead! long live rule-based information extraction systems! In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing; 2013.Google Scholar
  12. 12.
    Cohen W, McCallum A. Information extraction from the world wide web. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2003.Google Scholar
  13. 13.
    Cunningham H. Information extraction, automatic. In: Encyclopedia of language and linguistics. 2nd ed. Elsevier; Amsterdam. 2005.CrossRefGoogle Scholar
  14. 14.
    Doan A, Ramakrishnan R, Vaithyanathan S. Managing information extraction: state of the art and research directions. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2006.Google Scholar
  15. 15.
    Grishman R, Sundheim B. Message understanding conference-6: a brief history. In: Proceedings of the 16th International Conference on Computational Linguistics; 1996.Google Scholar
  16. 16.
    Huang J, Chen T, Doan A, Naughton JF. On the provenance of non-answers to queries over extracted data. Proc VLDB Endow;1(1):736–747CrossRefGoogle Scholar
  17. 17.
    Lafferty J, McCallum A, Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning; 2001.Google Scholar
  18. 18.
    Li Y, Chu V, Blohm S, Zhu H, Ho H. Facilitating pattern discovery for relation extraction with semantic-signature-based clustering. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management; 2011.Google Scholar
  19. 19.
    Li Y, Krishnamurthy R, Raghavan S, Vaithyanathan S, Jagadish HV. Regular expression learning for information extraction. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing; 2008.Google Scholar
  20. 20.
    Li Y, Krishnamurthy R, Vaithyanathan S, Jagadish H. Getting work done on the web: supporting transactional queries. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 2006.Google Scholar
  21. 21.
    Liu B, Chiticariu L, Chu V, Jagadish HV, Reiss F. Automatic rule refinement for information extraction.: Proc VLDB Endow. 2010;3(1):588–97.CrossRefGoogle Scholar
  22. 22.
    Nagesh A, Ramakrishnan G, Chiticariu L, Krishnamurthy R, Dharkar A, Bhattacharyya P. Towards efficient named-entity rule induction for customizability. In: Proceedings of the 2012 Conference on Empirical Methods on Natural Language Processing and Computational Natural Language Learning; 2012.Google Scholar
  23. 23.
    Reiss F, Raghavan S, Krishnamurthy R, Zhu H, Vaithyanathan S. An algebraic approach to rule-based information extraction. In: Proceedings of the 24th International Conference on Data Engineering; 2008.Google Scholar
  24. 24.
    Riloff E. Automatically constructing a dictionary for information extraction tasks. In: Proceedings of the 11th National Conference on Artificial Intelligence; 1993.Google Scholar
  25. 25.
    Roy S, Chiticariu L, Feldman V, Reiss F, Zhu H. Provenance-based dictionary refinement in information extraction. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2013.Google Scholar
  26. 26.
    Sarma AD, Jain A, Bohannon P. Building a generic debugger for information extraction pipelines. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management; 2011.Google Scholar
  27. 27.
    Sarma AD, Jain A, Srivastava D. I4e: interactive investigation of iterative information extraction. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2010.Google Scholar
  28. 28.
    Shen W, Doan A, Naughton J, Ramakrishnan R. Declarative information extraction using datalog with embedded extraction predicates. In: Proceedings of the 33rd International Conference on Very Large Data Bases; 2007.Google Scholar
  29. 29.
    Wandelt S, Deng D, Gerdjikov S, Mishra S, Mitankin P, Patil M, Siragusa E, Tiskin A, Wang W, Wang J, Leser U. State-of-the-art in string similarity search and join. SIGMOD Rec. 2014;43(1):64–76.CrossRefGoogle Scholar
  30. 30.
    Wang DZ, Wei L, Li Y, Reiss F, Vaithyanathan S. Selectivity estimation for extraction operators over text data. In: Proceedings of the 27th International Conference on Data Engineering; 2011.Google Scholar
  31. 31.
    Zhang C, Baldwin T, Ho H, Kimelfeld B, Li Y. Adaptive parser-centric text normalization. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics; 2013. p. 1159–68.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Laura Chiticariu
    • 1
  • Marina Danilevsky
    • 2
  • Howard Ho
    • 2
  • Rajasekar Krishnamurthy
    • 2
  • Yunyao Li
    • 2
  • Sriram Raghavan
    • 2
  • Frederick Reiss
    • 2
  • Shivakumar Vaithyanathan
    • 2
  • Huaiyu Zhu
    • 2
  1. 1.Scalable Natural Language ProcessingIBM Research – AlmadenSan JoseUSA
  2. 2.IBM Almaden Research CenterSan JoseUSA