神马文学网Web信息抽取技术纵览二
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第六章 总结和讨论
第 6.1. 节 总结 ...
第 6.2. 节 讨论 ...
第6.1.节 总结
信息抽取是近十年来新发展起来的领域。 MUC 等国际研讨会给予高度关注，并提出了评价这类系统的方法，定义了评价指标体系。
信息抽取技术的研究对象包括结构化、半结构化和自由式文档。对于自由式文档，多数采用了自然语言处理的方法，而其他两类文档的处理则多数是基于分隔符的。
网页是信息抽取技术研究的重点之一。通常用分装器从一特定网站上抽取信息。用一系列能处理不同网站的分装器就能将数据统一表示，并获得它们之间的关系。
分装器的建造通常是费事费力的，而且需要专门知识。加上网页动态变化，维护分装器的成本将很高。因此，如何自动构建分装器便成为主要的问题。通常采用的方法包括基于归纳学习的机器学习方法。
有若干研究系统被开发出来。这些系统使用机器学习算法针对网上信息源生成抽取规则。 ShopBot ， WIEN ， SoftMealy 和 STALKER 生成的分装器以分隔符为基础，能处理结构化程度高的网站。 RAPIER ， WHISK 和 SRV 能处理结构化程度稍差的信息源。所采用的抽取方法与传统的 IE 方法一脉相承，而学习算法多用关系学习法。
网站信息抽取和分装器生成技术可在一系列的应用领域内发挥作用。目前只有比价购物方面的商业应用比较成功，而最出色的系统包括 Jango ， Junglee 和 MySimon 。
第6.2.节 讨论
目前的搜索引擎并不能收集到网上数据库内的信息。根据用户的查询请求，搜索引擎能找到相关的网页，但不能把上面的信息抽取出来。“暗藏网”不断增加，因此有必要开发一些工具把相关信息从网页上抽取并收集起来。
由于网上信息整合越来越重要，虽然网站信息抽取的研究比较新，但将不断发展。机器学习方法的使用仍将成为主流方法，因为处理动态的海量信息需要自动化程度高的技术。在文献 [52] 中提出，结合不同类型的方法，以开发出适应性强的系统，这应是一个有前途的方向。在文献 [36] 中，一种混合语言知识和句法特征的方法也被提出来。
本文介绍的系统多数是针对 HTML 文档的。以后几年 XML 的使用将被普及。 HTML 描述的是文档的表现方式，是文档的格式语言。 XML 则可以告诉你文档的意义，即定义内容而不只是形式。这虽然使分装器的生成工作变得简单，但不能排除其存在的必要性。
将来的挑战是建造灵活和可升级的分装器自动归纳系统，以适应不断增长的动态网络的需要。
参考文献
[1] S. Abiteboul.
Querying Semistructured Data.
Proceedings of the International Conference on Database Theory (ICDT), ,
January 1997.
[2] B. Adelberg.
NoDoSE - A tool for Semi-Automatically Extracting Semistructured Data from Text
Documents.
Proceedings ACM SIGMOD International Conference on Management of Data, Seat-
tle, June 1998.
[3] D. E. Appelt, D. J. Israel.
Introduction to Information Extraction Technology.
Tutorial for IJCAI-99, , August 1999.
[4] N. Ashish, C. A. Knoblock.
Semi-automatic Wrapper Generation for Internet Information Sources.
Second IFCIS Conference on Cooperative Information Systems (CoopIS),
olina, June 1997.
[5] N. Ashish, C. A. Knoblock.
Wrapper Generation for semistructured Internet Sources.
SIGMOD Record, Vol. 26, No. 4, pp. 8--15, December 1997.
[6] P. Atzeni, G. Mecca.
Cut & Paste.
Proceedings of the 16‘th ACM SIGACT-SIGMOD-SIGART Symposium on Principles
of Database Systems (PODS‘97), , May 1997.
[7] M. Bauer, D. Dengler.
TrIAs - An Architecture for Trainable Information Assistants.
Workshop on AI and Information Integration, in conjunction with the 15‘th National
Conference on Artificial Intelligence (AAAI-98), , July 1998.
[8] P. Berka.
Intelligent Systems on the Internet.
http://lisp.vse.cz/ berka/ai-inet.htm, Laboratory of Intelligent Systems, University
of Economics,
[9] L. Bright, J. R. Gruser, L. Raschid, M. E. Vidal.
A Wrapper Generation Toolkit to Specify and Construct Wrappers for Web Accessible
Data Sources (WebSources).
Computer Systems Special Issue on Semantics on the WWW, Vol. 14 No. 2, March
1999.
[10] S. Brin.
Extracting Patterns and Relations from the World Wide Web.
International Workshop on the Web and Databases (WebDB‘98), , March 1998.
[11] M. E. Califf, R. J. Mooney.
Relational Learning of Pattern-Match Rules for Information Extraction.
Proceedings of the ACL Workshop on Natural Language , July 1997.
[12] M. E. Califf.
Relational Learning Techniques for Natural Language Information Extraction.
Ph.D. thesis, Department of Computer Sciences, , August
1998. Technical Report AI98-276.
[13] S. Chawathe, H. Garcia-Molina, J. Hammer, K. Ireland, Y. Papakonstantinou, J.
Ullman, J. Widom.
The TSIMMIS Project: Integration of Heterogeneous Information Sources.
In Proceedings of IPSJ Conference, pp. 7--18, , Japan, October 1994.
[14] B. Chidlovskii, U. M. Borghoff, P-Y. Chevalier.
Towards Sophisticated Wrapping of Web-based Information Repositories.
Proceedings of the 5‘th International RIAO Conference, , June 1997.
[15] M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, S. Slattery.
Learning to Extract Symbolic Knowledge from the World Wide Web.
Proceedings of the 15‘th National Conference on Artificial Intelligence (AAAI-98),
, , July 1998.
[16] M. Craven, S. Slattery, K. Nigam.
First-Order Learning for Web Mining.
Proceedings of the 10‘th European Conference on Machine , April
1998.
[17] R. B. Doorenbos, O. Etzioni, D. S. Weld.
A Scalable Comparison-Shopping Agent for the World Wide Web.
Technical report UW-CSE-, , 1996.
[18] R. B. Doorenbos, O. Etzioni, D. S. Weld.
A Scalable Comparison-Shopping Agent for the World-Wide-Web.
Proceedings of the first International Conference on Autonomous Agents, ,
February 1997.
[19] O. Etzioni
Moving up the Information Food Chain: Deploying Softbots on the World Wide Web.
AI Magazine, 18(2):11-18, 1997.
[20] D. Florescu, A. Levy, A. Mendelzon.
Database Techniques for the World Wide Web: A Survey.
ACM SIGMOD Record, Vol. 27, No. 3, September 1998.
[21] D. Freitag.
Information Extraction from HTML: Application of a General Machine Learning Ap-
proach.
Proceedings of the 15‘th National Conference on Artificial Intelligence (AAAI-98),
, , July 1998.
[22] D. Freitag.
Machine Learning for Information Extraction in Informal Domains.
Ph.D. dissertation, , November 1998.
[23] D. Freitag.
Multistrategy Learning for Information Extraction.
Proceedings of the 15‘th International Conference on Machine Learning (ICML-98),
, , July 1998.
[24] R. Gaizauskas, Y. Wilks.
Information Extraction: Beyond Document Retrieval.
Computational Linguistics and Chinese Language Processing, vol. 3, no. 2, pp. 17--60,
August 1998,
[25] H. Garcia-Molina, J. Hammer, K. Ireland, Y. Papakonstantinou, J. Ullman, J.
Widom.
Integrating and Accessing Heterogeneous Information Sources in TSIMMIS.
In Proceedings of the AAAI Symposium on Information Gathering, pp. 61--64, Stan-
ford, , March 1995.
[26] S. Grumbach and G. Mecca.
In Search of the Lost Schema.
Proceedings of the International Conference on Database Theory (ICDT‘99),
, January 1999.
[27] J-R. Gruser, L. Raschid, M. E. Vidal, L. Bright.
Wrapper Generation for Web Accessible Data Source.
Proceedings of the 3‘rd IFCIS International Conference on Cooperative Information
Systems (CoopIS-98), New York, August 1998.
[28] J. Hammer, H. Garcia-Molina, J. Cho, R. Aranha, A. Crespo.
Extracting Semistructured Information from Web.
Proceedings of the Workshop on Management of Semistructured Data, , Ari-
zona, May 1997.
[29] J. Hammer, H. Garcia-Molina, S. Nestorov, R. Yerneni, M. Breunig, V. Vassalos.
Template-Based Wrappers in the TSIMMIS System.
Proceedings of the 26‘th SIGMOD International Conference on Management of Data,
, , May 1997.
[30] C-H. Hsu.
Initial Results on Wrapping Semistructured Web Pages with Finite-State Transducers
and Contextual Rules.
Workshop on AI and Information Integration, in conjunction with the 15‘th National
Conference on Artificial Intelligence (AAAI-98), , July 1998.
[31] C-H. Hsu and M-T Dung.
Generating Finite-Sate Transducers for semistructured Data Extraction From the
Web.
Information systems, Vol 23. No. 8, pp. 521--538, 1998.
[32] C. A. Knoblock, S. Minton, J. L. Ambite, N. Ashish, P. J. Modi, I. Muslea, A. G.
Philpot, S. Tejada.
Modeling Web Sources for Information Integration.
Proceedings of the 15‘th National Conference on Artificial Intelligence (AAAI-98),
, , July 1998.
[33] N. Kushmerick, D. S. Weld, R. Doorenbos.
Wrapper Induction for Information Extraction.
15‘th International Joint Conference on Artificial Intelligence (IJCAI-97), ,
August 1997.
[34] N. Kushmerick.
Wrapper Induction for Information Extraction.
Ph.D. Dissertation, . Technical Report UW-CSE-,
1997.
[35] N. Kushmerick.
Wrapper induction: Efficiency and expressiveness.
Workshop on AI and Information Integration, in conjunction with the 15‘th National
Conference on Artificial Intelligence (AAAI-98), , July 1998.
[36] Kushmerick, N.
Gleaning the Web.
IEEE Intelligent Systems, 14(2), March/April 1999.
[37] S. Lawrence, C.l. Giles.
Searching the World Wide Web.
Science magazine, v. 280, pp. 98--100, April 1998.
[38] A. Y. Levy, A. Rajaraman, J. J. Ordille.
Querying Hetereogeneous Information Sources Using Source Descriptions.
Proceedings 22‘nd VLDB Conference, , September 1996.
[39] S. Muggleton, C. Feng.
Efficient Induction of Logic Programs.
Proceedings of the First Conference on Algorithmic Learning Theory, ,
1990.
[40]
Extraction Patterns: From Information Extraction to Wrapper Induction.
Information Sciences Institute, , 1998.
[41]
Extraction Patterns for Information Extraction Tasks: A Survey.
Workshop on Machine Learning for Information Extraction, , July 1999.
[42] Muslea, S. Minton, C. Knoblock.
STALKER: Learning Extraction Rules for Semistructured, Web-based Information
Sources.
Workshop on AI and Information Integration, in conjunction with the 15‘th National
Conference on Artificial Intelligence (AAAI-98), , July 1998.
[43] Muslea, S. Minton, C. Knoblock.
Wrapper Induction for Semistructured Web-based Information Sources.
Proceedings of the Conference on Automatic Learning and Discovery CONALD-98,
, June 1998.
[44] Muslea, S. Minton, C. Knoblock.
A Hierarchical Approach to Wrapper Induction.
Third International Conference on Autonomous Agents, (Agents‘99), Seattle, May
1999.
[45] S. Nestorov, S. Aboteboul, R. Motwani.
Inferring Structure in Semistructured Data.
Proceedings of the 13‘th International Conference on Data Engineering (ICDE‘97),
, , April 1997.
[46] STS Prasad, A. Rajaraman.
Virtual Database Technology, XML, and the Evolution of the Web.
Data Engineering, Vol. 21, No. 2, June 1998.
[47] J.R. Quinlan, R. M. Cameron-Jones.
FOIL: A Midterm Report.
European Conference on Machine Learning, , 1993.
[48] A. Rajaraman.
Transforming the Internet into a Database.
Workshop on Reuse of Web information, in conjunction with WWW7, Brisbane, April
1998.
[49] A. Sahuguet, F. Azavant.
WysiWyg Web Wrapper Factory (W
http://cheops.cis.upenn.edu/ sahuguet/WAPI/wapi.ps.gz,
nia, August 1998.
[50] D. Smith, M. Lopez.
Information Extraction for Semistructured Documents.
Proceedings of the Workshop on Management of Semistructured Data, in conjunction
with PODS/SIGMOD, , , May 1997.
[51] S. Soderland.
Learning to Extract Text-based Information from the World Wide Web.
Proceedings of the 3‘rd International Conference on Knowledge Discovery and Data
Mining (KDD), , August 1997.
[52] S. Soderland.
Learning Information Extraction Rules for Semistructured and Free Text.
Machine Learning, 1999.
[53] K. Zechner.
A Literature Survey on Information Extraction and Text Summarization.
Term paper, , 1997.
[54] About mySimon.
http://www.mysimon.com/about mysimon/company/backgrounder.anml
第 6.1. 节 总结 ...
第 6.2. 节 讨论 ...
第6.1.节 总结
信息抽取是近十年来新发展起来的领域。 MUC 等国际研讨会给予高度关注，并提出了评价这类系统的方法，定义了评价指标体系。
信息抽取技术的研究对象包括结构化、半结构化和自由式文档。对于自由式文档，多数采用了自然语言处理的方法，而其他两类文档的处理则多数是基于分隔符的。
网页是信息抽取技术研究的重点之一。通常用分装器从一特定网站上抽取信息。用一系列能处理不同网站的分装器就能将数据统一表示，并获得它们之间的关系。
分装器的建造通常是费事费力的，而且需要专门知识。加上网页动态变化，维护分装器的成本将很高。因此，如何自动构建分装器便成为主要的问题。通常采用的方法包括基于归纳学习的机器学习方法。
有若干研究系统被开发出来。这些系统使用机器学习算法针对网上信息源生成抽取规则。 ShopBot ， WIEN ， SoftMealy 和 STALKER 生成的分装器以分隔符为基础，能处理结构化程度高的网站。 RAPIER ， WHISK 和 SRV 能处理结构化程度稍差的信息源。所采用的抽取方法与传统的 IE 方法一脉相承，而学习算法多用关系学习法。
网站信息抽取和分装器生成技术可在一系列的应用领域内发挥作用。目前只有比价购物方面的商业应用比较成功，而最出色的系统包括 Jango ， Junglee 和 MySimon 。
第6.2.节 讨论
目前的搜索引擎并不能收集到网上数据库内的信息。根据用户的查询请求，搜索引擎能找到相关的网页，但不能把上面的信息抽取出来。“暗藏网”不断增加，因此有必要开发一些工具把相关信息从网页上抽取并收集起来。
由于网上信息整合越来越重要，虽然网站信息抽取的研究比较新，但将不断发展。机器学习方法的使用仍将成为主流方法，因为处理动态的海量信息需要自动化程度高的技术。在文献 [52] 中提出，结合不同类型的方法，以开发出适应性强的系统，这应是一个有前途的方向。在文献 [36] 中，一种混合语言知识和句法特征的方法也被提出来。
本文介绍的系统多数是针对 HTML 文档的。以后几年 XML 的使用将被普及。 HTML 描述的是文档的表现方式，是文档的格式语言。 XML 则可以告诉你文档的意义，即定义内容而不只是形式。这虽然使分装器的生成工作变得简单，但不能排除其存在的必要性。
将来的挑战是建造灵活和可升级的分装器自动归纳系统，以适应不断增长的动态网络的需要。
参考文献
[1] S. Abiteboul.
Querying Semistructured Data.
Proceedings of the International Conference on Database Theory (ICDT), ,
January 1997.
[2] B. Adelberg.
NoDoSE - A tool for Semi-Automatically Extracting Semistructured Data from Text
Documents.
Proceedings ACM SIGMOD International Conference on Management of Data, Seat-
tle, June 1998.
[3] D. E. Appelt, D. J. Israel.
Introduction to Information Extraction Technology.
Tutorial for IJCAI-99, , August 1999.
[4] N. Ashish, C. A. Knoblock.
Semi-automatic Wrapper Generation for Internet Information Sources.
Second IFCIS Conference on Cooperative Information Systems (CoopIS),
olina, June 1997.
[5] N. Ashish, C. A. Knoblock.
Wrapper Generation for semistructured Internet Sources.
SIGMOD Record, Vol. 26, No. 4, pp. 8--15, December 1997.
[6] P. Atzeni, G. Mecca.
Cut & Paste.
Proceedings of the 16‘th ACM SIGACT-SIGMOD-SIGART Symposium on Principles
of Database Systems (PODS‘97), , May 1997.
[7] M. Bauer, D. Dengler.
TrIAs - An Architecture for Trainable Information Assistants.
Workshop on AI and Information Integration, in conjunction with the 15‘th National
Conference on Artificial Intelligence (AAAI-98), , July 1998.
[8] P. Berka.
Intelligent Systems on the Internet.
http://lisp.vse.cz/ berka/ai-inet.htm, Laboratory of Intelligent Systems, University
of Economics,
[9] L. Bright, J. R. Gruser, L. Raschid, M. E. Vidal.
A Wrapper Generation Toolkit to Specify and Construct Wrappers for Web Accessible
Data Sources (WebSources).
Computer Systems Special Issue on Semantics on the WWW, Vol. 14 No. 2, March
1999.
[10] S. Brin.
Extracting Patterns and Relations from the World Wide Web.
International Workshop on the Web and Databases (WebDB‘98), , March 1998.
[11] M. E. Califf, R. J. Mooney.
Relational Learning of Pattern-Match Rules for Information Extraction.
Proceedings of the ACL Workshop on Natural Language , July 1997.
[12] M. E. Califf.
Relational Learning Techniques for Natural Language Information Extraction.
Ph.D. thesis, Department of Computer Sciences, , August
1998. Technical Report AI98-276.
[13] S. Chawathe, H. Garcia-Molina, J. Hammer, K. Ireland, Y. Papakonstantinou, J.
Ullman, J. Widom.
The TSIMMIS Project: Integration of Heterogeneous Information Sources.
In Proceedings of IPSJ Conference, pp. 7--18, , Japan, October 1994.
[14] B. Chidlovskii, U. M. Borghoff, P-Y. Chevalier.
Towards Sophisticated Wrapping of Web-based Information Repositories.
Proceedings of the 5‘th International RIAO Conference, , June 1997.
[15] M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, S. Slattery.
Learning to Extract Symbolic Knowledge from the World Wide Web.
Proceedings of the 15‘th National Conference on Artificial Intelligence (AAAI-98),
, , July 1998.
[16] M. Craven, S. Slattery, K. Nigam.
First-Order Learning for Web Mining.
Proceedings of the 10‘th European Conference on Machine , April
1998.
[17] R. B. Doorenbos, O. Etzioni, D. S. Weld.
A Scalable Comparison-Shopping Agent for the World Wide Web.
Technical report UW-CSE-, , 1996.
[18] R. B. Doorenbos, O. Etzioni, D. S. Weld.
A Scalable Comparison-Shopping Agent for the World-Wide-Web.
Proceedings of the first International Conference on Autonomous Agents, ,
February 1997.
[19] O. Etzioni
Moving up the Information Food Chain: Deploying Softbots on the World Wide Web.
AI Magazine, 18(2):11-18, 1997.
[20] D. Florescu, A. Levy, A. Mendelzon.
Database Techniques for the World Wide Web: A Survey.
ACM SIGMOD Record, Vol. 27, No. 3, September 1998.
[21] D. Freitag.
Information Extraction from HTML: Application of a General Machine Learning Ap-
proach.
Proceedings of the 15‘th National Conference on Artificial Intelligence (AAAI-98),
, , July 1998.
[22] D. Freitag.
Machine Learning for Information Extraction in Informal Domains.
Ph.D. dissertation, , November 1998.
[23] D. Freitag.
Multistrategy Learning for Information Extraction.
Proceedings of the 15‘th International Conference on Machine Learning (ICML-98),
, , July 1998.
[24] R. Gaizauskas, Y. Wilks.
Information Extraction: Beyond Document Retrieval.
Computational Linguistics and Chinese Language Processing, vol. 3, no. 2, pp. 17--60,
August 1998,
[25] H. Garcia-Molina, J. Hammer, K. Ireland, Y. Papakonstantinou, J. Ullman, J.
Widom.
Integrating and Accessing Heterogeneous Information Sources in TSIMMIS.
In Proceedings of the AAAI Symposium on Information Gathering, pp. 61--64, Stan-
ford, , March 1995.
[26] S. Grumbach and G. Mecca.
In Search of the Lost Schema.
Proceedings of the International Conference on Database Theory (ICDT‘99),
, January 1999.
[27] J-R. Gruser, L. Raschid, M. E. Vidal, L. Bright.
Wrapper Generation for Web Accessible Data Source.
Proceedings of the 3‘rd IFCIS International Conference on Cooperative Information
Systems (CoopIS-98), New York, August 1998.
[28] J. Hammer, H. Garcia-Molina, J. Cho, R. Aranha, A. Crespo.
Extracting Semistructured Information from Web.
Proceedings of the Workshop on Management of Semistructured Data, , Ari-
zona, May 1997.
[29] J. Hammer, H. Garcia-Molina, S. Nestorov, R. Yerneni, M. Breunig, V. Vassalos.
Template-Based Wrappers in the TSIMMIS System.
Proceedings of the 26‘th SIGMOD International Conference on Management of Data,
, , May 1997.
[30] C-H. Hsu.
Initial Results on Wrapping Semistructured Web Pages with Finite-State Transducers
and Contextual Rules.
Workshop on AI and Information Integration, in conjunction with the 15‘th National
Conference on Artificial Intelligence (AAAI-98), , July 1998.
[31] C-H. Hsu and M-T Dung.
Generating Finite-Sate Transducers for semistructured Data Extraction From the
Web.
Information systems, Vol 23. No. 8, pp. 521--538, 1998.
[32] C. A. Knoblock, S. Minton, J. L. Ambite, N. Ashish, P. J. Modi, I. Muslea, A. G.
Philpot, S. Tejada.
Modeling Web Sources for Information Integration.
Proceedings of the 15‘th National Conference on Artificial Intelligence (AAAI-98),
, , July 1998.
[33] N. Kushmerick, D. S. Weld, R. Doorenbos.
Wrapper Induction for Information Extraction.
15‘th International Joint Conference on Artificial Intelligence (IJCAI-97), ,
August 1997.
[34] N. Kushmerick.
Wrapper Induction for Information Extraction.
Ph.D. Dissertation, . Technical Report UW-CSE-,
1997.
[35] N. Kushmerick.
Wrapper induction: Efficiency and expressiveness.
Workshop on AI and Information Integration, in conjunction with the 15‘th National
Conference on Artificial Intelligence (AAAI-98), , July 1998.
[36] Kushmerick, N.
Gleaning the Web.
IEEE Intelligent Systems, 14(2), March/April 1999.
[37] S. Lawrence, C.l. Giles.
Searching the World Wide Web.
Science magazine, v. 280, pp. 98--100, April 1998.
[38] A. Y. Levy, A. Rajaraman, J. J. Ordille.
Querying Hetereogeneous Information Sources Using Source Descriptions.
Proceedings 22‘nd VLDB Conference, , September 1996.
[39] S. Muggleton, C. Feng.
Efficient Induction of Logic Programs.
Proceedings of the First Conference on Algorithmic Learning Theory, ,
1990.
[40]
Extraction Patterns: From Information Extraction to Wrapper Induction.
Information Sciences Institute, , 1998.
[41]
Extraction Patterns for Information Extraction Tasks: A Survey.
Workshop on Machine Learning for Information Extraction, , July 1999.
[42] Muslea, S. Minton, C. Knoblock.
STALKER: Learning Extraction Rules for Semistructured, Web-based Information
Sources.
Workshop on AI and Information Integration, in conjunction with the 15‘th National
Conference on Artificial Intelligence (AAAI-98), , July 1998.
[43] Muslea, S. Minton, C. Knoblock.
Wrapper Induction for Semistructured Web-based Information Sources.
Proceedings of the Conference on Automatic Learning and Discovery CONALD-98,
, June 1998.
[44] Muslea, S. Minton, C. Knoblock.
A Hierarchical Approach to Wrapper Induction.
Third International Conference on Autonomous Agents, (Agents‘99), Seattle, May
1999.
[45] S. Nestorov, S. Aboteboul, R. Motwani.
Inferring Structure in Semistructured Data.
Proceedings of the 13‘th International Conference on Data Engineering (ICDE‘97),
, , April 1997.
[46] STS Prasad, A. Rajaraman.
Virtual Database Technology, XML, and the Evolution of the Web.
Data Engineering, Vol. 21, No. 2, June 1998.
[47] J.R. Quinlan, R. M. Cameron-Jones.
FOIL: A Midterm Report.
European Conference on Machine Learning, , 1993.
[48] A. Rajaraman.
Transforming the Internet into a Database.
Workshop on Reuse of Web information, in conjunction with WWW7, Brisbane, April
1998.
[49] A. Sahuguet, F. Azavant.
WysiWyg Web Wrapper Factory (W
http://cheops.cis.upenn.edu/ sahuguet/WAPI/wapi.ps.gz,
nia, August 1998.
[50] D. Smith, M. Lopez.
Information Extraction for Semistructured Documents.
Proceedings of the Workshop on Management of Semistructured Data, in conjunction
with PODS/SIGMOD, , , May 1997.
[51] S. Soderland.
Learning to Extract Text-based Information from the World Wide Web.
Proceedings of the 3‘rd International Conference on Knowledge Discovery and Data
Mining (KDD), , August 1997.
[52] S. Soderland.
Learning Information Extraction Rules for Semistructured and Free Text.
Machine Learning, 1999.
[53] K. Zechner.
A Literature Survey on Information Extraction and Text Summarization.
Term paper, , 1997.
[54] About mySimon.
http://www.mysimon.com/about mysimon/company/backgrounder.anml
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