A maximum likelihood framework for integrating taxonomies

Authors:
Suju Rajan;Kunal Punera;Joydeep Ghosh
Affiliations:
Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas;Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas;Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, Texas
Venue:
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Year:
2005

Citing 15
Cited 1

Algorithms for clustering data

Algorithms for clustering data
Hierarchical classification of Web content

SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
On integrating catalogs

Proceedings of the 10th international conference on World Wide Web
Learning to map between ontologies on the semantic web

Proceedings of the 11th international conference on World Wide Web
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The Chimaera Ontology Environment

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies

The VLDB Journal — The International Journal on Very Large Data Bases
Cross-training: learning probabilistic mappings between topics

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Web taxonomy integration through co-bootstrapping

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A hierarchical method for multi-class support vector machines

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Integrating multiple internet directories by instance-based learning

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
FCA-MERGE: bottom-up merging of ontologies

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1

Learning Link-Based Naïve Bayes Classifiers from Ontology-Extended Distributed Data

OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

We define a generalized strategy eliminability criterion for bimatrix games that considers whether a given strategy is eliminable relative to given dominator & eliminee subsets of the players' strategies. We show that this definition spans a spectrum ...