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Tech Report kmi-04-18 Abstract


Semi-Automatic Population of Ontologies from Text
Techreport ID: kmi-04-18
Date: 2004
Author(s): David Celjuska, Maria Vargas-Vera
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This paper describes a system for semi-automatic population of ontologies with instances from unstructured text. The system is based on supervised learning and therefore learns extraction rules from annotated text and then applies those rules on newly documents for ontology population. It is based on three componentes: Marmot, a natural language processor; Crystal, a dictionary induction tool; and Badger, an information extraction tool. The important part of the entire cycle is a user who accepts, rejects or modifies newly extracted and suggested instances to be populated. A description of experiments performed with text corpus consisting of 91 documents is given in turn. The results cover the paper and support a presented hypothesis of assigning a rule confi-dence value to each extraction rule to improve the performance.
 
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Knowledge Management is...


Knowledge Management
Creating learning organisations hinges on managing knowledge at many levels. Knowledge can be provided by individuals or it can be created as a collective effort of a group working together towards a common goal, it can be situated as "war stories" or it can be generalised as guidelines, it can be described informally as comments in a natural language, pictures and technical drawings or it can be formalised as mathematical formulae and rules, it can be expressed explicitly or it can be tacit, embedded in the work product. The recipient of knowledge - the learner - can be an individual or a work group, professionals, university students, schoolchildren or informal communities of interest.
Our aim is to capture, analyse and organise knowledge, regardless of its origin and form and make it available to the learner when needed presented with the necessary context and in a form supporting the learning processes.