This is a reformatted version of the original dissertation, and

The rapidly growing volume of electronic information available in distributed heterogeneous environments, such as the World Wide Web, has made it increasingly difficult and time-consuming to search for and locate relevant documents (in textual, visual or audio format). To relieve users of the burden of this task, intelligent tools that automate the search and retrieval tasks by generating profiles of user interests with minimal user interaction are required. In this paper, an intelligent knowledge filtering system (SAMURAI) and, in particular, the text summarisation and clustering modules of the system are described. Modules for extracting salient concepts in documents were built and evaluated on a variety of documents from different knowledge domains. A natural language processing approach based on part-of-speech tagging was used and compared with an alternative approach based on the well-known (and commonly-used) TFIDF information retrieval algorithm. Results show that, in the tagger...

I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii The explosive growth in the number of documents produced daily necessitates the development of effective alternatives to explore, analyze, and discover knowledge from documents. Document mining research work has emerged to devise automated means to discover and analyze useful information from documents. This work has been mainly concerned with constructing text representation models, developing distance measures to estimate similarities between documents, and utilizing that in mining processes such as document clustering, document classification, information retrieval, information filtering, and information extraction. Conventional text representation methodologies consider documents as bags of words and ignore the meanings and ideas their authors want to convey. It is this

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The rapidly growing volume of electronic information available on distributed heterogeneous environments, such as the World Wide Web, has made it increasingly difficult and time-consuming to search for and locate relevant documents (in textual, visual or audio format). To relieve users of the burden of this task, the building of intelligent tools that automate the search, filtering and retrieval tasks by generating profiles of user interests with minimal user interaction is required. In this paper we present an intelligent knowledge filtering system (SAMURAI) and, in particular, describe the text summarisation and clustering modules of the system. We have developed and tested a part-of-speech tagger for extracting salient concepts in documents and have evaluated its performance by testing the system on a variety of documents from different knowledge domains. Comparative performance with the well-known (and commonly-used) TFIDF information retrieval algorithm was also undertaken. Result...

This paper presents the design and state of development of a framework for the construction and use of ontologies to guide searches in the Web or in document repositories. The aim is to enhance precision and recall in information retrieval sessions through the use of a context associated to each session. For transparency and flexibility, these contexts are dynamically built by the user from the system's available ontologies.

Attribute-based naming enables powerful search and organization tools for ever-increasing user data sets. However, such tools are only useful in combination with accurate attribute assignment. Existing systems rely on user input and content analysis, but they have enjoyed minimal success. This paper discusses new approaches to automatically assigning attributes to files, including several forms of context analysis, which has been highly successful in the Google web search engine. With extensions like application hints (e.g., web links for downloaded files) and inter-file relationships, it should be possible to infer useful attributes for many files, making attribute-based search tools more effective.

Attribute-based naming enables powerful search and organization tools for ever-increasing user data sets. However, such tools are only useful in combination with accurate attribute assignment. Existing systems rely on user input and content analysis, but they have enjoyed minimal success. This paper discusses new approaches to automatically assigning attributes to files, including several forms of context analysis, which has been highly successful in the Google web search engine. With extensions like application hints (e.g., web links for downloaded files) and inter-file relationships, it should be possible to infer useful attributes for many files, making attribute-based search tools more effective.