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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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Cited By

Song JBai LAn XZhou L(2025)Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learningNeurocomputing10.1016/j.neucom.2024.129019617(129019)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129019
Ma RGao BWang WWang LWang XZhao L(2025)MHEC: One-shot relational learning of knowledge graphs completion based on multi-hop information enhancementNeurocomputing10.1016/j.neucom.2024.128760614(128760)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128760
Mao RGe MHan SLi WHe KZhu LCambria E(2025)A survey on pragmatic processing techniquesInformation Fusion10.1016/j.inffus.2024.102712114(102712)Online publication date: Feb-2025
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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.

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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New York, NY, United States

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Published: 20 May 2012

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SIGMOD/PODS '12

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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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Cited By

Song JBai LAn XZhou L(2025)Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learningNeurocomputing10.1016/j.neucom.2024.129019617(129019)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129019
Ma RGao BWang WWang LWang XZhao L(2025)MHEC: One-shot relational learning of knowledge graphs completion based on multi-hop information enhancementNeurocomputing10.1016/j.neucom.2024.128760614(128760)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128760
Mao RGe MHan SLi WHe KZhu LCambria E(2025)A survey on pragmatic processing techniquesInformation Fusion10.1016/j.inffus.2024.102712114(102712)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102712
Qiu ZHuang GQin XWang YWang JZhou Y(2024)A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English TextsInformation10.3390/info1511070815:11(708)Online publication date: 5-Nov-2024
https://doi.org/10.3390/info15110708
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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
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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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Komarlu TJiang MWang XHan JBaeza-Yates RBonchi F(2024)OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity TypingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671745(1407-1417)Online publication date: 25-Aug-2024
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Zhao ZDou ZZhou YSerra ESpezzano F(2024)Generating Intent-aware Clarifying Questions in Conversational Information Retrieval SystemsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679851(3384-3394)Online publication date: 21-Oct-2024
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