Induction of recursive transfer rules
Learning language in logic
Learning for text categorization and information extraction with ILP
Learning language in logic
Inductive Logic Programming for Natural Language Processing
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Learning ontologies from natural language texts
International Journal of Human-Computer Studies
Automatic grammar induction and parsing free text: a transformation-based approach
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
The state of the art in ontology learning: a framework for comparison
The Knowledge Engineering Review
Discovering non-taxonomic relations from the web
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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The discovery of relationships between concepts is a crucial point in ontology learning (OL). In most cases, OL is achieved from a collection of domain-specific texts, describing the concepts of the domain and their relationships. A natural way to represent the description associated to a particular text is to use a structured term (or tree). We present a method for learning transformation rules, rewriting natural language texts into trees, where the input examples are couples (text, tree). The learning process produces an ordered set of rules such that, applying these rules to a textgives the corresponding tree.