Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
CREAM: CREAting metadata for the Semantic Web
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: The Semantic Web: an evolution for a revolution
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
KIM – a semantic platform for information extraction and retrieval
Natural Language Engineering
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites
Computational Linguistics
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This paper overviews and analyses the on-going research attempts to apply language technologies to automatic ontology acquisition. At first glance there are many successful approaches in this very hot field. However, most of them aim at the extraction of named entities as well as draft taxonomies and partonomies. Only few attempts exist for enriching ontologies by applying word-sense disambiguation. There are principle obstacles to extract automatically coherent conceptualisations from raw texts: it is impossible to identify exactly the types and their instances as well as the word meanings which denote types. It is also impossible to validate a text-based conceptual model against the real world. Thus we can expect only partial success in the semi-automatic acquisition in specific (limited) domains, by workbenches supporting the human knowledge engineer in the final ontological choices.