demonstration

Learning to detect abnormal semantic web data

Authors:

Yang Yu,

Xingjian Zhang,

Jeff HeflinAuthors Info & Claims

K-CAP '11: Proceedings of the sixth international conference on Knowledge capture

Pages 177 - 178

https://doi.org/10.1145/1999676.1999713

Published: 26 June 2011 Publication History

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Abstract

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References

[1]

H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan. GATE: A framework and graphical development environment for robust NLP tools and applications. In Proceedings of the 40th Annual Meeting of the ACL, 2002.

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C. Fürber and M. Hepp. Using semantic web resources for data quality management. In P. Cimiano and H. S. Pinto, editors, EKAW, volume 6317 of Lecture Notes in Computer Science, pages 211--225. Springer, 2010.

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Y. Lei, V. Uren, and E. Motta. A framework for evaluating semantic metadata. In Proceedings of the 4th international conference on Knowledge capture, K-CAP '07, pages 135--142, New York, NY, USA, 2007. ACM.

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D. Maynard, W. Peters, and Y. Li. Metrics for evaluation of ontology-based information extraction. In WWW 2006 Workshop on ¡±Evaluation of Ontologies for the Web, 2006.

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S. Rendle, L. B. Marinho, A. Nanopoulos, and L. Schmidt-Thieme. Learning optimal ranking with tensor factorization for tag recommendation. In KDD'09, pages 727--736, 2009.

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Index Terms

Learning to detect abnormal semantic web data
1. Computing methodologies
  1. Machine learning
    1. Learning settings

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Information & Contributors

Information

Published In

K-CAP '11: Proceedings of the sixth international conference on Knowledge capture

June 2011

212 pages

ISBN:9781450303965

DOI:10.1145/1999676

General Chair:
Mark A. Musen
Stanford University, USA
,
Program Chair:
Oscar Corcho
Universidad Politécnica de Madrid, Spain

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2011

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Demonstration

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K-CAP '2011

Sponsor:

SIGAI

K-CAP '2011: Knowledge Capture Conference

June 26 - 29, 2011

Alberta, Banff, Canada

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Overall Acceptance Rate 55 of 198 submissions, 28%

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Semantic Web Mining

DL-Learner Structured Machine Learning on Semantic Web Data

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