Verity Inc. has developed a comprehensive suite of tools for accurately and efficiently organizing enterprise content which involves four basic steps: (i) creating taxonomies, (ii) building classification models, (iii) populating taxonomies with documents, and (iv) deploying populated taxonomies in enterprise portals. A taxonomy is a hierarchical representation of categories. A taxonomy provides a navigation structure for exploring and understanding the underlying corpus without sifting through a huge volume of documents. Thematic Mapping automatically discovers a concept tree from a corpus of unstructured documents and assigns meaningful labels to concepts based on a semantic network. Integrating with Verity Intelligent Classifier's user-friendly GUI, a user can drill down a concept tree for navigation, perform a conceptual search to retrieve documents pertaining to a concept, build a taxonomy from the concept tree, as well as edit a taxonomy to tailor it into various views (customized taxonomies) of the same corpus. Classification rules can be generated automatically from concepts. These classification rules can be used for populating documents into the taxonomy.

Despite the advancement of XML, the majority of documents on the Web is still marked up with HTML for visual rendering purposes only, thus building a huge amount of legacy data. In order to facilitate querying Web based data in a way more efficient and effective than just keyword based retrieval, enriching such Web documents with both structure and semantics is necessary. We describe a novel approach to the integration of topic specific HTML documents into a repository of XML documents. In particular, we describe how topic specific HTML documents are transformed into XML documents. The proposed document transformation and semantic element tagging process utilizes document restructuring rules and minimum information about the topic in the form of concepts. For the resulting XML documents, a majority schema is derived that describes common structures among the documents in the form of a DTD. We explore and discuss different techniques, and rules for document conversion and majority schema discovery. We finally demonstrate the feasibility and effectiveness of our approach by applying it to a set of resume HTML documents gathered by a Web crawler.

As information volume in enterprise systems and in the Web grows rapidly, how to accurately retrieve information is an important research area. Several corpus based smoothing techniques have been proposed to address the data sparsity and synonym problems faced by information retrieval systems. Such smoothing techniques are often unable to discover and utilize the correlations among terms.We propose CVS, a Correlation-Verification based Smoothing method, that considers co-occurrence information in smoothing. Strongly correlated terms in a document are identified by their co-occurrence frequencies in the document. To avoid missing correlated terms with low co-occurrence frequencies but specific to the theme of the document, the joint distributions of terms in the document are compared with those in the corpus for statistical significance.A common approach to apply corpus based smoothing techniques to information retrieval is by refining the vector representations of documents. This paper investigates the effects of corpus based smoothing on information retrieval by query expansion using term clusters generated from a term clustering process. The results can also be viewed in light of the effects of smoothing on clustering.Empirical studies show that our approach outperforms previous corpus based smoothing techniques. It improves retrieval effectiveness by 14.6%. The results demonstrate that corpus based smoothing can be used for query expansion by term clustering.