7 - Indira Gandhi Centre for Atomic Research

3.4 Classification approaches
This approach is based on grouping data according to similarities and classes. Bayesian
approach that uses probabilities and a graphical means of representation is considered a
type of classification. Bayesian networks are typically used when uncertainty associated
with an outcome can be expressed in terms of a probability. This approach relies on
encoded domain knowledge and has been used for diagnostics systems. Other examples of
classification approaches are decision tree approach and pattern discovery and data
cleaning models. Decision trees are hierarchical structures, where each internal node
contains a test on an attribute; each branch corresponds to an outcome of the test, and each
leaf node gives a prediction for the value of the class variable. Classification approach is
useful for organizing the potential metadata of digital library for knowledge generation
process.
4. Knowledge Discovery in Digital Libraries
The Library has been the center of the preservation, utilization and distribution of
information and knowledge. Digital Library has a much greater capacity for Knowledge
Management. The current Digital Library Architecture should include classification and
thesaurus – the vocabulary control and knowledge organizing tools, which serves three
purposes in a traditional library, the description, organization and retrieval of information.
For more effective and efficient exploration, the networked information should be prearranged
together with vigorous improvement of search techniques. Classification and
thesauri that contain condensed intelligence can be used in organizing networked
information especially metadata to facilitate the information resources usability and
catalyze the Digital Library into Knowledge Management.
Classification and thesaurus can be merged into a concept network and the metadata can
be distributed into the nodes of the network according their subjects. The abstract concept
node substantiated with the related metadata records becomes a knowledge node. This
forms a consistent knowledge network that is not only a framework for resource
organization, but also a structure for knowledge navigation, retrieval, and learning.
The bibliographic data is one of the most important resources of library, which will be
useful for knowledge discovery process. Based on the subject indexing, the bibliographic
data can be combined with the classification and thesaurus to form a knowledge structure,
which provides a skeleton for organization of bibliographic data. Corpus knowledge can
be formed when new terms can be extracted automatically from the bibliographic data to
update the classification and thesaurus. Such a knowledge network provides the user with
an opportunity for navigation, searching and learning.
5. Conclusion
Knowledge discovery process has evolved and continues to evolve from the intersection of
research fields such as machine learning, pattern recognition, database statistics, Artificial
Intelligence etc. While Knowledge Discovery tools hold the promise of an enabling
technology that could unlock the knowledge lying dormant in huge databases, they suffer
some shortcomings such as problems in representing multiple interrelated relations in
databases, incremental rule generation when database is expanded, and finally consistency
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