research-article

WebChild: harvesting and organizing commonsense knowledge from the web

Authors:

Gerard de Melo,

Fabian Suchanek,

Gerhard WeikumAuthors Info & Claims

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Pages 523 - 532

https://doi.org/10.1145/2556195.2556245

Published: 24 February 2014 Publication History

Abstract

This paper presents a method for automatically constructing a large commonsense knowledge base, called WebChild, from Web contents. WebChild contains triples that connect nouns with adjectives via fine-grained relations like hasShape, hasTaste, evokesEmotion, etc. The arguments of these assertions, nouns and adjectives, are disambiguated by mapping them onto their proper WordNet senses. Our method is based on semi-supervised Label Propagation over graphs of noisy candidate assertions. We automatically derive seeds from WordNet and by pattern matching from Web text collections. The Label Propagation algorithm provides us with domain sets and range sets for 19 different relations, and with confidence-ranked assertions between WordNet senses. Large-scale experiments demonstrate the high accuracy (more than 80 percent) and coverage (more than four million fine grained disambiguated assertions) of WebChild.

References

[1]

L. von Ahn, M. Kedia, M. Blum: Verbosity: a Game for Collecting Common-Sense Facts. CHI 2006.

Digital Library

[2]

A. Almuhareb, M. Poesio: Attribute-Based and Value-Based Clustering: An Evaluation. EMNLP 2004.

[3]

S. Auer, C. Bizer, J. Lehmann, G. Kobilarov, R. Cyganiak, Z. Ives: DBpedia: A Nucleus for a Web of Open Data. ISWC/ASWC 2007.

Digital Library

[4]

M. Baroni, R. Zamparelli: Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space. EMNLP 2010.

Digital Library

[5]

E. Blanco, H. C. Cankaya, and D. Moldovan: Commonsense Knowledge Extraction using Concepts Properties. FLAIRS 2011.

[6]

K. Bollacker, C. Evans, P. Paritosh, T. Sturge, J. Taylor: Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge. SIGMOD 2008

Digital Library

[7]

T. Brants, A. Franz: Web 1T 5-gram Version 1. Linguistic Data Consortium, 2006.

[8]

L.D. Brown, T.T. Cai, A. Dasgupta: Interval Estimation for a Binomial Proportion. Statistical Science 16: 101--133, 2001.

[9]

B. B. Dalvi, W. W. Cohen, J. Callan. Websets: Extracting Sets of Entities from the Web using Unsupervised Information Extraction. WSDM 2012.

Digital Library

[10]

L. Del Corro, R. Gemulla. ClausIE: clause-based open information extraction. WWW 2013.

Digital Library

[11]

G. de Melo, M. Bansal: Good, Great, Excellent: Global Inference of Semantic Intensities. Transactions of the ACL, 2013.

[12]

C.D. Fellbaum, G.A. Miller (Eds.): WordNet: An Electronic Lexical Database. MIT Press, 1998.

[13]

M. Hartung, A. Frank: A Structured Vector Space Model for Hidden Attribute Meaning in Adjective-Noun Phrases. COLING 2010.

Digital Library

[14]

M. Hartung, A. Frank: Exploring Supervised LDA Models for Assigning Attributes to Adjective-Noun Phrases. EMNLP 2011.

Digital Library

[15]

C. Havasi, R. Speer, J. Alonso: ConceptNet 3: a Flexible, Multilingual Semantic Network for Common Sense Knowledge. RANLP 2007.

[16]

A. Herdagdelen, M. Baroni: Bootstrapping a Game with a Purpose for Commonsense Collection. ACM TIST 3(4): 59 (2012)

Digital Library

[17]

G. Hirst, D. St-Onge: Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms. In {12}, 1998.

[18]

IBM Journal of Research and Development 56(3/4), Special Issue on "This is Watson", 2012.

[19]

C. Leacock and M. Chodorow: Combining local context and WordNet similarity for word sense identification. Fellbaum 1998, pp. 265--28

[20]

G.E. Lebani, E. Pianta: Encoding Commonsense Lexical Knowledge into WordNet. Global WordNet Conference 2012.

[21]

Douglas B. Lenat: CYC: A Large-Scale Investment in Knowledge Infrastructure. Comm. of the ACM 38(11), pp. 32-38, 1995.

Digital Library

[22]

M. Lesk: Automatic Sense Disambiguation using Machine Readable Dictionaries: How to Tell a Pine Cone from an Ice Cream Cone. SIGDOC 1986.

Digital Library

[23]

H. Liu, P. Singh: ConceptNet: a Practical Commonsense Reasoning Toolkit. BT Technology Journal, 2004.

Digital Library

[24]

B. Liu: Sentiment Analysis and Opinion Mining. Morgan & Claypool, 2012.

Digital Library

[25]

S. Banerjee, T. Pedersen: An Adapted Lesk Algorithm for Word Sense Disambiguation using WordNet. CICLing 2002.

Digital Library

[26]

W. Derry, P. Pratim Talukdar, T. Mitchell. PIDGIN: ontology alignment using web text as interlingua. CIKM 2013.

Digital Library

[27]

A. Fader, S. Soderland, O. Etzioni. Identifying relations for open information extraction. EMNLP 2011.

Digital Library

[28]

S. Siersdorfer, J. Hare: Analyzing and Predicting Sentiment of Images on the Social Web. ACM Multimedia 2010.

Digital Library

[29]

A. Singhal: Introducing the Knowledge Graph: Things, Not Strings. googleblog.blogspot.co.uk, 16 May 2012.

[30]

R. Speer, C. Havasi, H. Surana: Using Verbosity: Common Sense Data from Games with a Purpose. FLAIRS 2010.

[31]

R. Speer, C. Havasi: Representing General Relational Knowledge in ConceptNet 5. LREC 2012.

[32]

S. Staab, R. Studer (Eds.): Handbook on Ontologies, Springer, 2009.

Digital Library

[33]

F.M. Suchanek, G. Kasneci, G. Weikum: YAGO: A Core of Semantic Knowledge. WWW 2007.

Digital Library

[34]

P.P. Talukdar, K. Crammer: New Regularized Algorithms for Transductive Learning. ECML/PKDD 2009.

Digital Library

[35]

N. Tandon, G. de Melo, G. Weikum: Deriving a Web-Scale Common Sense Fact Database. AAAI 2011.

[36]

R.C. Wang, W.W. Cohen: Language-Independent Set Expansion of Named Entities Using the Web. ICDM 2007.

Digital Library

[37]

X. Zhu, Z. Ghahramani, J.D. Lafferty: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML 2003.

Cited By

Nguyen TRazniewski SWeikum GSerra ESpezzano F(2024)Cultural Commonsense Knowledge for Intercultural DialoguesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679768(1774-1784)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679768
Wang DHu LHao RShao YLv XNie LLi JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Let Me Show You Step by Step: An Interpretable Graph Routing Network for Knowledge-based Visual Question AnsweringProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657790(1984-1994)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657790
Wang XHuang RJin ZFang TQu H(2024): Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language ModelsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332715330:1(273-283)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327153
Show More Cited By

Index Terms

WebChild: harvesting and organizing commonsense knowledge from the web
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

February 2014

712 pages

ISBN:9781450323512

DOI:10.1145/2556195

General Chairs:
Ben Carterette
University of Delaware, USA
,
Fernando Diaz
Microsoft Research, USA
,
Program Chairs:
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Donald Metzler
Google, USA

Copyright © 2014 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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Published: 24 February 2014

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February 24 - 28, 2014

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WSDM '14 Paper Acceptance Rate 64 of 355 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Nguyen TRazniewski SWeikum GSerra ESpezzano F(2024)Cultural Commonsense Knowledge for Intercultural DialoguesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679768(1774-1784)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679768
Wang DHu LHao RShao YLv XNie LLi JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Let Me Show You Step by Step: An Interpretable Graph Routing Network for Knowledge-based Visual Question AnsweringProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657790(1984-1994)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657790
Wang XHuang RJin ZFang TQu H(2024): Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language ModelsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332715330:1(273-283)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327153
Xu NLu ZTian HKang RCao JZhang YLiu A(2024)Learning to Supervise Knowledge Retrieval Over a Tree Structure for Visual Question AnsweringIEEE Transactions on Multimedia10.1109/TMM.2024.335563826(6689-6700)Online publication date: 18-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3355638
Song YSun PLiu HLi ZSong WXiao YZhou X(2024)Scene-Driven Multimodal Knowledge Graph Construction for Embodied AIIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339974636:11(6962-6976)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3399746
Xu NGao YLiu ATian HZhang Y(2024)Multi-Modal Validation and Domain Interaction Learning for Knowledge-Based Visual Question AnsweringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338427036:11(6628-6640)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3384270
Qu YYang JZhang WChen WYu H(2024)Data-Product Catalogues: Envisioning with Knowledge-aware Natural Language Processing2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00023(45-54)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00023
Gao LZhang HLiu YSheng NFeng HXu H(2024)PGCLExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124011251:COnline publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124011
He MFang TWang WSong Y(2024)Acquiring and Modeling Abstract Commonsense Knowledge via ConceptualizationArtificial Intelligence10.1016/j.artint.2024.104149(104149)Online publication date: May-2024
https://doi.org/10.1016/j.artint.2024.104149
Wang CChen TLiu J(2024)Don’t Ignore the Drive of Curiosity: Rethinking Subtleties Between Universality of Commonsense Knowledge and Excellence of Large Language ModelsSN Computer Science10.1007/s42979-024-03165-w5:6Online publication date: 15-Aug-2024
https://doi.org/10.1007/s42979-024-03165-w
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