Automatic Erroneous Data Detection over Type-Annotated Linked Data

Author:

RAHOMAN Md-Mizanur1,ICHISE Ryutaro12

Affiliation:

1. SOKENDAI (The Graduate University for Advanced Studies)

2. National Institute of Informatics

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Reference23 articles.

1. [1] M. Acosta, A. Zaveri, E. Simperl, D. Kontokostas, S. Auer, and J. Lehmann, “Crowdsourcing linked data quality assessment,” In Proceedings of the 12th International Semantic Web Conference, vol.8219, pp.260-276, 2013.

2. [2] M. Breunig, H.-P. Kriegel, R.T. Ng, and J. Sander, “Optics-of: Identifying local outliers,” In Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery, vol.1704, pp.262-270, 1999.

3. [3] M.M. Breunig, H.-P. Kriegel, R.T. Ng, and J. Sander, “Lof: Identifying density-based local outliers,” SIGMOD Record, vol.29, no.2, pp.93-104, May 2000.

4. [4] V. Bryl and C. Bizer, “Learning conflict resolution strategies for cross-language wikipedia data fusion,” In Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, pp.1129-1134, 2014.

5. [5] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv., vol.41, no.3, pp.15:1-15:58, July 2009.

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