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Tech Report kmi-03-04 Abstract


A Comparative Study of Term Weighting Methods for Information Filtering
Techreport ID: kmi-03-04
Date: 2003
Author(s): Nikolaos Nanas, Victoria Uren, Anne De Roeck
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The users of an information filtering system can only be expected to provide a small amount of information to initialize their user profile. Therefore, term weighting methods for information filtering have somewhat different requirements to those for information retrieval and text categorization. We present a comparative evaluation of term weighting methods, including one novel method, relative document frequency, designed specifically for information filtering. The best weighting methods appear to be those which balance exploiting user input and data from the collection.
 
KMi Publications Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

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.