research-article

Probase: a probabilistic taxonomy for text understanding

Authors:

Kenny Q. ZhuAuthors Info & Claims

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

Pages 481 - 492

https://doi.org/10.1145/2213836.2213891

Published: 20 May 2012 Publication History

Abstract

Knowledge is indispensable to understanding. The ongoing information explosion highlights the need to enable machines to better understand electronic text in human language. Much work has been devoted to creating universal ontologies or taxonomies for this purpose. However, none of the existing ontologies has the needed depth and breadth for universal understanding. In this paper, we present a universal, probabilistic taxonomy that is more comprehensive than any existing ones. It contains 2.7 million concepts harnessed automatically from a corpus of 1.68 billion web pages. Unlike traditional taxonomies that treat knowledge as black and white, it uses probabilities to model inconsistent, ambiguous and uncertain information it contains. We present details of how the taxonomy is constructed, its probabilistic modeling, and its potential applications in text understanding.

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Index Terms

Probase: a probabilistic taxonomy for text understanding
1. Information systems
  1. Information retrieval
  2. Information storage systems

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cover image ACM Conferences

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

May 2012

886 pages

ISBN:9781450312479

DOI:10.1145/2213836

General Chairs:
K. Selçuk Candan
Arizona State University
,
Yi Chen
Arizona State University
,
Richard Snodgrass
University of Arizona
,
Program Chair:
Luis Gravano
Columbia University
,
Publications Chair:
Ariel Fuxman
Microsoft Research

Copyright © 2012 ACM.

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Published: 20 May 2012

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SIGMOD

SIGMOD/PODS '12: International Conference on Management of Data

May 20 - 24, 2012

Arizona, Scottsdale, USA

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SIGMOD '12 Paper Acceptance Rate 48 of 289 submissions, 17%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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https://dl.acm.org/doi/10.1145/3660521
Geeganage DXu YLi Y(2024)A Semantics-enhanced Topic Modelling Technique: Semantic-LDAACM Transactions on Knowledge Discovery from Data10.1145/363940918:4(1-27)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3639409
Ouyang SHuang JPillai PZhang YZhang YHan JBaeza-Yates RBonchi F(2024)Ontology Enrichment for Effective Fine-grained Entity TypingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671857(2318-2327)Online publication date: 25-Aug-2024
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