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Article

SemTag and seeker: bootstrapping the semantic web via automated semantic annotation

Authors:

Anant Jhingran,

Sridhar Rajagopalan,

Andrew Tomkins,

John A. Tomlin,

Jason Y. ZienAuthors Info & Claims

WWW '03: Proceedings of the 12th international conference on World Wide Web

Pages 178 - 186

https://doi.org/10.1145/775152.775178

Published: 20 May 2003 Publication History

Abstract

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.

References

[1]

S. Abiteboul, D. Quass, J. McHugh, J. Widom, and J. Wiener. The lorel query language for semistructured data. International Journal of Digital Libraries, 1(1):68--88, 1997.

[2]

R. Agrawal, R. Bayardo, D. Gruhl, and S. Papadimitriou. Vinci: A service-oriented architecture for rapid development of web applications. In Proceedings of the Tenth International World Wide Web Conference (WWW2001), pages 355--365, Hong Kong, China, 2001.

Digital Library

[3]

AltaVista. http://www.altavista.com.

[4]

G. Arocena, A. Mendelzon, and G. Mihaila. Applications of a Web query language. In Proceedings of the 6th International World Wide Web Conference (WWW1997), pages 1305--1315, Santa Clara, CA, 1997.

Digital Library

[5]

T. Berners-Lee, J. Hendler, and O. Lassila. Semantic web. Scientific American, 1(1):68--88, 2000.

[6]

D. Box, D. Ehnebuske, G. Kakivaya, A. Layman, N. Mendelsohn, H. F. Nielsen, S. Thatte, and D. Winder. Simple Object Access Protocol. http://www.w3.org/TR/SOAP/, May 2000.

[7]

D. Brickley and R.V.Guha. Rdf schema. http://www.w3.org/TR/rdf-schema/.

[8]

A. Broder and M. R. Henzinger. Algorithmic aspects of information retrieval on the web. In M. G. C. R. J. Abello, P. M. Pardalos, editor, Handbook of Massive Data Sets. Kluwer Academic Publishers, Boston, to appear.

Digital Library

[9]

C. Clarke, G. Cormack, and F. Burkowski. Shortest substring ranking. In Proceedings of the Fourth Text Retrieval Conference, pages 295--304, Gaithersburg, MD, November 1995.

[10]

W. Cohen and L. Jensen. A structured wrapper induction system for extracting information from semi-structured documents. In Proceedings of the Workshop on Adaptive Text Extraction and Mining (IJCAI'01), 2001.

[11]

M. Erdmann, A. Maedche, H. Schnurr, and S. Staab. From manual to semi-automatic semantic annotation: About ontology-based text annotation tools. In P. Buitelaar and K. Hasida, editors, Proceedings of the COLING 2000 Workshop on Semantic Annotation and Intelligent Content, August 2000.

Digital Library

[12]

Google. http://www.google.com.

[13]

T. R. Gruber. Towards Principles for the Design of Ontologies Used for Knowledge Sharing. In N. Guarino and R. Poli, editors, Formal Ontology in Conceptual Analysis and Knowledge Representation, Deventer, The Netherlands, 1993. Kluwer Academic Publishers.

[14]

J. Heflin and J. Hendler. Searching the web with shoe. In AAAI-2000 Workshop on AI for Web Search, 2000.

[15]

J. M. Hellerstein, M. J. Franklin, S. Chandrasekaran, A. Deshpande, K. Hilldrum, D. Maden, V. Raman, and M. A. Shah. Adaptive query processing: Technology in evolution. IEEE Data Engineering Bulletin, 23(2):7--18, 2000.

[16]

J. Hirai, S. Raghavan, A. Paepcke, and H. Garcia-Molina. WebBase: A repository of Web pages. In Proceedings of the 9th International World Wide Web Conference (WWW2000), pages 277-293, Amsterdam, The Netherlands, 2000.

Digital Library

[17]

J. Kahan and M.-R. Koivunen. Annotea: an open RDF infrastructure for shared web annotations. In World Wide Web, pages 623--632, 2001.

Digital Library

[18]

N. Kushmerick, D. S. Weld, and R. B. Doorenbos. Wrapper induction for information extraction. In Intl. Joint Conference on Artificial Intelligence (IJCAI), pages 729--737, 1997.

[19]

T. Leonard and H. Glaser. Large scale acquisition and maintenance from the web without source access. http://semannot2001.aifb.uni-karlsruhe.de/positionpapers/Leonard.pdf, 2001.

[20]

K. Lerman, C. Knoblock, and S. Minton. Automatic data extraction from lists and tables in web sources. In IJCAI-2001 Workshop on Adaptive Text Extraction and Mining, August 2001.

[21]

G.-A. Levow. Corpus-based techniques for word sense disambiguation. Technical Report AIM-1637, MIT AI Lab, 1, 1997.

Digital Library

[22]

J. Li, L. Zhang, and Y. Yu. Learning to generate semantic annotation for domain specific sentences. http://semannot2001.aifb.uni-karlsruhe.de/positionpapers/GenerateSemAnnot.pdf.

[23]

P. K. Lockheed. AeroDAML: Applying information extraction to generate DAML annotations from web pages.

[24]

D. L. McGuinness. Description logics emerge from ivory towers. In Description Logics, 2001.

[25]

G. Mecca, A. Mendelzon, and P. Merialdo. Efficient queries over web views. In Proceedings of the 6th International Conference on Extending Database Technology (EDBT'98), volume LNCS 1377, pages 72--86, Valencia, Spain, 1998. Springer-Verlag.

Digital Library

[26]

R. Mihalcea. Word sense disambiguation and its application to the internet search. Master's thesis, Southern Methodist University, 1999.

[27]

A. Newell. Some problems of the basic organization in problem-solving programs. In Proceedings of the Second Conference on Self-Organizing Systems, pages 393--423, Washington, DC, 1962.

[28]

N. F. Noy, M. Sintek, S. Decker, M. Crubezy, R. W. Fergerson, and M. A. Musen. Creating semantic web contents with protege-2000. IEEE Intelligent Systems, 2(16):60--71, 2001.

Digital Library

[29]

J. Pustejovsky, B. Boguraev, M. Verhagen, P. Buitelaar, and M. Johnston. Semantic indexing and typed hyperlinking. In Proceedings of the American Association for Artificial Intelligence Conference, Spring Symposium, NLP for WWW, pages 120--128, 1997.

[30]

R.Guha and R. McCool. Tap: Towards a web of data. http://tap.stanford.edu/.

[31]

E. Riloff and J. Shepherd. A corpus-based approach for building semantic lexicons. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing (EMNLP-97), pages 117--124, Providence, RI, 1997.

[32]

H. Schütze. Automatic word sense discrimination. Computational Linguistics, 24(1):97--124, 1998.

Digital Library

[33]

E. Spertus and L. A. Stein. Squeal: A structured query language for the web. In Proceedings of the 9th International World Wide Web Conference (WWW2000), pages 95--103, Amsterdam, The Netherlands, 2000.

Digital Library

[34]

S. Staab, A. Maedche, and S. Handschuh. An annotation framework for the semantic web. In S. Isjizaki, editor, Proceedings of the First Workshop on Multimedia Annotation, Tokyo, Japan, January 2001.

[35]

The Internet Archive. http://www.archive.org.

[36]

M. Vargas-Vera, E. Motta, J. Domingue, M. Lanzoni, A. Stutt, and F. Ciravegna. MnM: Ontology driven semi-automatic and automatic support for semantic markup. In The 13th International Conference on Knowledge Engineering and Management (EKAW 2002), 2002.

Digital Library

[37]

W3C. Platform for internet content selection. http://www.w3.org/PICS/.

[38]

W3C. Web ontology language. http://www.w3.org/2001/sw/WebOnt/.

[39]

Web-in-a-Box. http://research.compaq.com/SRC/WebArcheology/wib.html.

[40]

Y. Wilks and M. Stevenson. Sense tagging: Semantic tagging with a lexicon. In Proceedings of the SIGLEX Workshop Tagging Text with Lexical Semantics: What, why and how?, pages 47--51, 1997.

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SemTag and seeker: bootstrapping the semantic web via automated semantic annotation

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Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '03: Proceedings of the 12th international conference on World Wide Web

May 2003

772 pages

ISBN:1581136803

DOI:10.1145/775152

Conference Chairs:
Gusztáv Hencsey
MTA SZTAKI, Hungary
,
Bebo White
Stanford Linear Accelerator Center, USA
,
Program Chairs:
Yih-Farn Robin Chen
AT&T Labs -- Research, USA
,
László Kovács
MTA SZTAKI, Hungary
,
Steve Lawrence
Google Inc., USA

Copyright © 2003 ACM.

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Published: 20 May 2003

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https://doi.org/10.1007/978-981-97-9437-9_27
Tang YLi SYu JMa J(2024)CAW: Confidence-Based Adaptive Weighted Model for Multi-modal Entity LinkingArtificial Neural Networks and Machine Learning – ICANN 202410.1007/978-3-031-72347-6_3(34-51)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72347-6_3
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