Probase: A probabilistic taxonomy for text understanding

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2012, Pages 481-492

  Wu, Wentao ^a   Li, Hongsong ^b   Wang, Haixun ^b   Zhu, Kenny Q ^c  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b MICROSOFT RESEARCH ASIA   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

knowledgebase; taxonomy; text understanding

Indexed keywords

HUMAN LANGUAGE; INFORMATION EXPLOSION; KNOWLEDGE BASE; POTENTIAL APPLICATIONS; PROBABILISTIC MODELING; TEXT UNDERSTANDING; UNCERTAIN INFORMATIONS;

DATABASE SYSTEMS;

TAXONOMIES;

EID: 84862653732     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2213836.2213891     Document Type: Conference Paper

References (40)

1. S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives. Dbpedia: A nucleus for a web of open data. In ISWC/ASWC, pages 722-735, 2007.
2. M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni. Open information extraction from the web. In IJCAI, pages 2670-2676, 2007.
3. D. Blei and J. Lafferty. Topic models. In Text Mining: Classification, Clustering, and Applications. Taylor & Francis, 2009.
4. P. Bloom. Glue for the mental world. Nature, 421:212-213, Jan 2003.
5. K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In SIGMOD, 2008.
6. S. A. Caraballo. Automatic construction of a hypernym-labeled noun hierarchy from text. In ACL, 1999.
7. 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, 2010.
8. P. Cimiano, A. Pivk, L. S. Thieme, and S. Staab. Learning taxonomic relations from heterogeneous sources of evidence. In Proceedings of the ECAI 2004 Ontology Learning and Population Workshop, 2004.
9. B. Ding, H. Wang, , R. Jin, J. Han, and Z. Wang. Optimizing index for taxonomy keyword search. In SIGMOD, 2012.
10. D. Downey, M. Broadhead, and O. Etzioni. Locating complex named entities in web text. In IJCAI, pages 2733-2739, 2007.
11. D. Downey, O. Etzioni, and S. Soderland. A probabilistic model of redundancy in information extraction. In IJCAI, 2005.
12. O. Etzioni, M. J. Cafarella, D. Downey, S. Kok, A.-M. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates. Web-scale information extraction in knowitall. In WWW, pages 100-110, 2004.
13. C. Fellbaum, editor. WordNet: an electronic lexical database. MIT Press, 1998.
14. M. Fleischman. Automated subcategorization of named entities. In ACL (Companion Volume), pages 25-30, 2001.
15. M. Fleischman and E. H. Hovy. Fine grained classification of named entities. In COLING, 2002.
16. M. A. Hearst. Automatic acquisition of hyponyms from large text corpora. In COLING, pages 539-545, 1992.
17. T. Lee, Z. Wang, H. Wang, and S. Hwang. Web scale taxonomy cleansing. In VLDB, 2011.
18. D. B. Lenat and R. V. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley, 1989.
19. P. Li, H. Wang, H. Li, and X. Wu. Sparse information extraction based on semantic contexts. Under submission, 2012.
20. Z. Li, H. Li, H. Wang, and X. Zhou. Overcoming semantic drift in web-scale information extraction. Under submission, 2012.
21. C. Matuszek, M. J. Witbrock, R. C. Kahlert, J. Cabral, D. Schneider, P. Shah, and D. B. Lenat. Searching for common sense: Populating cyc from the web. In AAAI, pages 1430-1435, 2005.
22. G. Murphy. The big book of concepts. The MIT Press, 2004.
23. D. Nadeau and S. Sekine. A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1):3-26, 2007.
24. R. Navigli. Word sense disambiguation: A survey. ACM Comput. Surv., 41(2), 2009.
25. M. Pasca. Organizing and searching the world wide web of facts -step two: harnessing the wisdom of the crowds. In WWW, 2007.
26. S. P. Ponzetto and M. Strube. Deriving a large-scale taxonomy from wikipedia. In AAAI, 2007.
27. A. Ritter, S. Soderland, and O. Etzioni. What is this, anyway: Automatic hypernym discovery. In AAAI Spring Symposium on Learning by Reading and Learning to Read, 2009.
28. E. Segal, D. Koller, and D. Ormoneit. Probabilistic abstraction hierarchies. In NIPS, pages 913-920, 2001.
29. B. Shao, H. Wang, and Y. Li. The Trinity graph engine. Technical report, Microsoft Research, 2012.
30. B. Shao, H. Wang, and Y. Xiao. Managing and mining large graphs: Systems and implementations. In SIGMOD, 2012.
31. P. Singh, T. Lin, E. Mueller, G. Lim, T. Perkins, and W. Li Zhu. Open Mind Common Sense: Knowledge acquisition from the general public. On the Move to Meaningful Internet Systems: CoopIS, DOA, and ODBASE, pages 1223-1237, 2002.
32. R. Snow, D. Jurafsky, and A. Y. Ng. Semantic taxonomy induction from heterogenous evidence. In ACL, 2006.
33. R. Snow, S. Prakash, D. Jurafsky, and A. Y. Ng. Learning to merge word senses. In EMNLP-CoNLL, pages 1005-1014, 2007.
34. Y. Song, H. Wang, Z. Wang, H. Li, and W. Chen. Short text conceptualization using a probabilistic knowledgebase. In IJCAI, 2011.
35. F. M. Suchanek, G. Kasneci, and G. Weikum. Yago: a core of semantic knowledge. In WWW, pages 697-706, 2007.
36. C. Thomas, P. Mehra, R. Brooks, and A. P. Sheth. Growing fields of interest - using an expand and reduce strategy for domain model extraction. In Web Intelligence, pages 496-502, 2008.
37. J. Wang, Z. Wang, H. Wang, and K. Q. Zhu. Understanding tables on the web. Technical report, Microsoft Research, 2010.
38. S. Wang, Y. Song, H. Wang, and Z. Zhang. On understanding short texts. Under submission, 2012.
39. Y. Wang, H. Li, H. Wang, and K. Q. Zhu. Toward topic search on the web. Technical report, Microsoft Research, 2010.
40. W. Wu, H. Li, H. Wang, and K. Q. Zhu. Probase: A probabilistic taxonomy for text understanding. Technical report, Microsoft Research, 2012