| Ontologies serve as a means for establishing a conceptually concise basis for communicating knowledge for many purposes. Ontology modeling and maintenance is a time consuming task. Human expert modeling by hand is biased, error prone, and expensive. It is very difficult and cumbersome to manually derive ontologies from data. This appears to be true even regardless of the type of data one might consider. In the workshop we plan to attract researchers that try to overcome the problem through learning ontologies from natural language text, semi-structured data (e.g., HTML or XML) or structured data such as found in databases. Natural language texts exhibit morphological, syntactic, semantic, pragmatic and conceptual constraints that interact in order to convey a particular meaning to the reader. Thus, the text transports information to the reader and the reader embeds this information into his background knowledge. Through the understanding of the text data is associated with conceptual structures and new conceptual structures are learned from the interacting constraints given through language. Tools that learn ontologies from natural language exploit the interacting constraints on the various language levels (from morphology to pragmatics and background knowledge) in order to discover new concepts and stipulate relationships between concepts. The Text-To-Onto System developed in the course of the project ystem provides an integrated environment for the task of learning ontologies learning from text. | |
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