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Generating hypotheses from the web

Published: 21 April 2008 Publication History

Abstract

Hypothesis generation is a crucial initial step for making scientific discoveries. This paper addresses the problem of automatically discovering interesting hypotheses from the web. Given a query containing one or two entities of interest, our algorithm automatically generates a semantic profile describing the specified entity or provides the potential connections between two entities of interest. We implemented a prototype on top of the Google search engine and the experimental results demonstrate the effectiveness of our algorithms.

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Srinivasan, P. Text Mining: Generating Hypotheses from MEDLINE. JASIST 55(5): 396--413, 2004.
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Das-Neves, F., Fox, E. A. and Yu, X. Connecting Topics in Document Collections with Stepping Stones and Pathways. CIKM '05, ACM Press, Bremen, Ger., Nov. 2005, pp. 91--98.
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Luo, G., Tang, C. and Tian, Y. Answering Relationship Queries on the Web. WWW2007, pp. 561-5-5-570.
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Jin, W., Srihari, R. K., Ho, H. and Wu, X. Improving Knowledge Discovery in Document Collections through Combining Text Retrieval and Link Analysis Techniques. (ICDM'07), pp.193--202.

Cited By

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  • (2016)Risk Mining: Company-Risk Identification from Unstructured Sources2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2016.0191(1308-1311)Online publication date: Dec-2016
  • (2015)Improving Cross-Document Knowledge Discovery Through Content and Link Analysis of Wikipedia KnowledgeTransactions on Large-Scale Data- and Knowledge-Centered Systems XXI10.1007/978-3-662-47804-2_8(161-184)Online publication date: 17-Jul-2015
  • (2010)Discovering, ranking and annotating cross-document relationships between conceptsProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871768(1929-1930)Online publication date: 26-Oct-2010

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cover image ACM Conferences
WWW '08: Proceedings of the 17th international conference on World Wide Web
April 2008
1326 pages
ISBN:9781605580852
DOI:10.1145/1367497
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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Author Tags

  1. hypothesis generation
  2. link analysis
  3. web search

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2016)Risk Mining: Company-Risk Identification from Unstructured Sources2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2016.0191(1308-1311)Online publication date: Dec-2016
  • (2015)Improving Cross-Document Knowledge Discovery Through Content and Link Analysis of Wikipedia KnowledgeTransactions on Large-Scale Data- and Knowledge-Centered Systems XXI10.1007/978-3-662-47804-2_8(161-184)Online publication date: 17-Jul-2015
  • (2010)Discovering, ranking and annotating cross-document relationships between conceptsProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871768(1929-1930)Online publication date: 26-Oct-2010

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