The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Proceedings of the 10th international conference on World Wide Web
Ontologies: a silver bullet for knowledge management and electronic commerce
Ontologies: a silver bullet for knowledge management and electronic commerce
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Information Retrieval
Machine Learning
Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European 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
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Facilitating the Exchange of Explicit Knowledge through Ontology Mappings
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Text categorization by boosting automatically extracted concepts
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An introduction to boosting and leveraging
Advanced lectures on machine learning
Cross-training: learning probabilistic mappings between topics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Classifying search engine queries using the web as background knowledge
ACM SIGKDD Explorations Newsletter
An experimental comparative study of web mining methods for recommender systems
DIWED'06 Proceedings of the 6th WSEAS International Conference on Distance Learning and Web Engineering
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Web Semantics: Science, Services and Agents on the World Wide Web
Automatic maintenance of web directories by mining web browsing data
Journal of Web Engineering
PROBABILISTIC HEURISTICS FOR HIERARCHICAL WEB DATA CLUSTERING
Computational Intelligence
| Hi-index | 0.00 |
We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects from the source taxonomies into categories of the master taxonomy. However, conventional machine learning algorithms totally ignore the availability of the source taxonomies. In fact, source and master taxonomies often have common categories under different names or other more complex semantic overlaps. We introduce two techniques that exploit the semantic overlap between the source and master taxonomies to build better classifiers for the master taxonomy. The first technique, Cluster Shrinkage, biases the learning algorithm against splitting source categories by making objects in the same category appear more similar to each other. The second technique, Co-Bootstrapping, tries to facilitate the exploitation of inter-taxonomy relationships by providing category indicator functions as additional features for the objects. Our experiments with real-world Web data show that these proposed add-on techniques can enhance various machine learning algorithms to achieve substantial improvements in performance for taxonomy integration.