Internetdokument,
Learning from Web: Review of Approaches
(13.04.2005)
Zusammenfassung
Knowledge discovery is defined as non-trivial extraction of implicit,
previously unknown and potentially useful information from given data.
Knowledge extraction from web documents deals with unstructured, free-format
documents whose number is enormous and rapidly growing. The artificial neural
networks are well suitable to solve a problem of knowledge discovery from web
documents because trained networks are able more accurately and easily to
classify the learning and testing examples those represent the text mining
domain. However, the neural networks that consist of large number of weighted
connections and activation units often generate the incomprehensible and
hard-to-understand models of text classification. This problem may be also
addressed to most powerful recurrent neural networks that employ the feedback
links from hidden or output units to their input units. Due to feedback links,
recurrent neural networks are able take into account of a context in document.
To be useful for data mining, self-organizing neural network techniques of
knowledge extraction have been explored and developed. Self-organization
principles were used to create an adequate neural-network structure and reduce
a dimensionality of features used to describe text documents. The use of these
principles seems interesting because ones are able to reduce a neural-network
redundancy and considerably facilitate the knowledge representation.
