Richard T Freeman | PubMed Health

PubMed2005

Web content management by self-organization.

We present a new method for content management and knowledge discovery using a topology-preserving neural network. The method, termed topological organization of content (TOC), can generate a taxonomy of topics from a set of unannotated, unstructured documents. The TOC consists of a hierarchy of self-organizing growing chains (GCs), each of which can develop independently in terms of size and topics. The dynamic development process is validated continuously using a proposed entropy-based Bayesian information criterion (BIC). Each chain meeting the criterion spans child chains, with reduced vocabularies and increased specializations. This results in a topological tree hierarchy, which can be browsed like a table of contents directory or web portal. A brief review is given on existing methods for document clustering and organization, and clustering validation measures. The proposed approach has been tested and compared with several existing methods on real world web page datasets. The results have clearly demonstrated the advantages and efficiency in content organization of the proposed method in terms of computational cost and representation. The TOC can be easily adapted for large-scale applications. The topology provides a unique, additional feature for retrieving related topics and confining the search space.

Abstracting and Indexing↗

PubMedDate not supplied

Adaptive topological tree structure for document organisation and visualisation.

The self-organising map (SOM) is finding more and more applications in a wide range of fields, such as clustering, pattern recognition and visualisation. It has also been employed in knowledge management and information retrieval. We propose an alternative to existing 2-dimensional SOM based methods for document analysis. The method, termed Adaptive Topological Tree Structure (ATTS), generates a taxonomy of underlying topics from a set of unclassified, unstructured documents. The ATTS consists of a hierarchy of adaptive self-organising chains, each of which is validated independently using a proposed entropy-based Bayesian information criterion. A node meeting the expansion criterion spans a child chain, with reduced vocabulary and increased specialisation. The ATTS creates a topological tree of topics, which can be browsed like a content hierarchy and reflects the connections between related topics at each level. A review is also given on the existing neural network based methods for document clustering and organisation. Experimental results on real-world datasets using the proposed ATTS method are presented and compared with other approaches. The results demonstrate the advantages of the proposed validation criteria and the efficiency of the ATTS approach for document organisation, visualisation and search. It shows that the proposed methods not only improve the clustering results but also boost the retrieval.

Bayes Theorem↗