Representation and learning in information retrieval
Representation and learning in information retrieval
Machine Learning
Robust Classification for Imprecise Environments
Machine Learning
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ontologies for Enterprise Knowledge Management
IEEE Intelligent Systems
In search of deterministic methods for initializing K-means and Gaussian mixture clustering
Intelligent Data Analysis
A framework for handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
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This paper discusses the problem of predicting the structural changes in an ontology. It addresses ontologies that contain instances in addition to concepts. The focus is on an ontology where the instances are textual documents, but the approach presented in this document is general enough to also work with other kinds of instances, as long as a similarity measure can be defined over them. We examine the changes in the Open Directory Project ontology of Web pages over a period of several years and analyze the most common types of structural changes that took place during that time. We then present an approach for predicting one of the more common types of structural changes, namely the addition of a new concept that becomes the subconcept of an existing parent concept and adopts a few instances of this existing parent concept. We describe how this task can be formulated as a machine-learning problem and present an experimental evaluation of this approach that shows promising results of the proposed approach.