Harvesting facts from textual web sources by constrained label propagation

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2011, Pages 837-846

  Wang, Yafang ^a   Yang, Bin ^a   Qu, Lizhen ^a   Spaniol, Marc ^a   Weikum, Gerhard ^a  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

knowledge harvesting; label propagation; temporal facts

Indexed keywords

KNOWLEDGE HARVESTING; LABEL PROPAGATION; LARGE KNOWLEDGE BASIS; LOSS FUNCTIONS; ONLINE NEWS; TEMPORAL FACTS; TEXT SOURCES; TIME-AWARENESS; WEB SOURCES; WIKIPEDIA;

KNOWLEDGE MANAGEMENT; WEBSITES;

HARVESTING;

EID: 83055191277     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063576.2063698     Document Type: Conference Paper

References (31)

1. S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. G. Ives. Dbpedia: A nucleus for a web of open data. In ISWC/ASWC, pages 722-735, 2007.
2. S. Baluja, R. Seth, D. Sivakumar, Y. Jing, J. Yagnik, S. Kumar, D. Ravichandran, and M. Aly. Video suggestion and discovery for youtube: taking random walks through the view graph. In WWW, pages 895-904, 2008.
3. M. S. Bazaraa, H. D. Sherali, and C. M. Shetty. Nonlinear programming: theory and algorithms. 1993.
4. K. Berberich, S. J. Bedathur, O. Alonso, and G. Weikum. A language modeling approach for temporal information needs. In ECIR, pages 13-25, 2010.
5. B. Boguraev, J. Pustejovsky, R. K. Ando, and M. Verhagen. Timebank evolution as a community resource for timeml parsing. Language Resources and Evaluation, pages 91-115, 2007.
6. A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. H. Jr., and T. M. Mitchell. Toward an architecture for never-ending language learning. In AAAI, pages 1306-1313, 2010.
7. J. Chen, D.-H. Ji, C. L. Tan, and Z.-Y. Niu. Relation extraction using label propagation based semi-supervised learning. In ACL, 2006.
8. A. Culotta and J. S. Sorensen. Dependency tree kernels for relation extraction. In ACL, pages 423-429, 2004.
9. O. Etzioni, M. Banko, S. Soderland, and D. S. Weld. Open information extraction from the web. Commun. ACM, 51(12):68-74, 2008.
10. T. Heath and C. Bizer. Linked Data: Evolving the Web into a Global Data Space. Morgan & Claypool, 2011.
11. X. Ling and D. S. Weld. Temporal information extraction. In AAAI, 2010.
12. M. Mintz, S. Bills, R. Snow, and D. Jurafsky. Distant supervision for relation extraction without labeled data. In ACL/AFNLP, pages 1003-1011, 2009.
13. N. Nakashole, M. Theobald, and G. Weikum. Scalable knowledge harvesting with high precision and high recall. In WSDM, pages 227-236, 2011.
14. M. Pasca. Towards temporal web search. In SAC, pages 1117-1121, 2008.
15. D. Rao and D. Yarowsky. Ranking and semi-supervised classification on large scale graphs using map-reduce. In Graph-based Methods for Natural Language Processing, pages 58-65, 2009.
16. C. E. Rasmussen. Gaussian processes for machine learning. MIT Press, 2006.
17. F. M. Suchanek, G. Kasneci, and G. Weikum. Yago: a core of semantic knowledge. In WWW, pages 697-706. ACM, 2007.
18. P. P. Talukdar and K. Crammer. New regularized algorithms for transductive learning. In ECML/PKDD (2), pages 442-457, 2009.
19. P. P. Talukdar and F. Pereira. Experiments in graph-based semi-supervised learning methods for class-instance acquisition. In ACL, pages 1473-1481, 2010.
20. M. Verhagen, R. Gaizauskas, F. Schilder, M. Hepple, J. Moszkowicz, and J. Pustejovsky. The tempeval challenge: identifying temporal relations in text. Language Resources and Evaluation, 43:161-179, 2009.
21. M. Verhagen, I. Mani, R. Sauri, J. Littman, R. Knippen, S. B. Jang, A. Rumshisky, J. Phillips, and J. Pustejovsky. Automating temporal annotation with tarsqi. In ACL, 2005.
22. Y. Wang, M. Yahya, and M. Theobald. Time-aware reasoning in uncertain knowledge bases. In MUD, pages 51-65, 2010.
23. Y. Wang, M. Zhu, L. Qu, M. Spaniol, and G. Weikum. Timely yago: harvesting, querying, and visualizing temporal knowledge from wikipedia. In EDBT, pages 697-700, 2010.
24. F. Wu and D. S. Weld. Automatically refining the wikipedia infobox ontology. In WWW, pages 635-644, 2008.
25. F. Xu, H. Uszkoreit, and H. Li. A seed-driven bottom-up machine learning framework for extracting relations of various complexity. In ACL, 2007.
26. L. Yao, S. Riedel, and A. McCallum. Collective cross-document relation extraction without labelled data. In EMNLP, pages 1013-1023, 2010.
27. Q. Zhang, F. Suchanek, and G. Weikum. TOB: Timely ontologies for business relations. In WebDB.
28. Z. Zhang. Weakly-supervised relation classification for information extraction. In CIKM, pages 581-588, 2004.
29. G. Zhou, M. Zhang, D.-H. Ji, and Q. Zhu. Tree kernel-based relation extraction with context-sensitive structured parse tree information. In EMNLP-CoNLL, pages 728-736, 2007.
30. J. Zhu, Z. Nie, X. Liu, B. Zhang, and J.-R. Wen. Statsnowball: a statistical approach to extracting entity relationships. In WWW, pages 101-110, 2009.
31. X. Zhu, Z. Ghahramani, and J. D. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, pages 912-919, 2003.