Knowledge Mining

Text mining is an exciting application field and an area of scientific research that is currently under rapid development. It uses techniques from well-established scientific fields (e.g. data mining, machine learning, information retrieval, natural language processing, case-based reasoning, statistics and knowledge management) in an effort to help people gain insight, understand and interpret large quantities of (usually) semi-structured and unstructured data. Despite the advances made during the last few years, many issues remain unresolved.

Knowledge Mining: A Quantitative Synthesis of Research Results and Findings.- An Evidential Approach to Classification Combination for Text Categorisation.- Visualization Techniques for Non Symmetrical Relations.- Understanding Text Mining: A Pragmatic Approach.- Novel Approaches to Unsupervised Clustering Through k-Windows Algorithm.- Semiometric Approach, Qualitative Research and Text Mining Techniques for Modelling the Material Culture of Happiness.- Semantic Distances for Sets of Senses and Applications in Word Sense Disambiguation.- A Strategic Roadmap for Text Mining.- Text Mining Applied to Multilingual Corpora.- Content Annotation for the Semantic Web.- An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them.- Extraction of the Useful Words from a Decisional Corpus. Contribution of Correspondence Analysis.- Collective SME Approach to Technology Watch and Competitive Intelligence: The Role of Intermediate Centers.- New Challenges and Roles of Metadata in Text/Data Mining in Statistics.- Using Text Mining in Official Statistics.- Combining Text Mining and Information Retrieval Techniques for Enhanced Access to Statistical Data on the Web: A Preliminary Report.- Comparative Study of Text Mining Tools.- Some Industrial Applications of Text Mining.- Using Text Mining Tools for Event Data Analysis.- Terminology Extraction: An Analysis of Linguistic and Statistical Approaches.- Analysis of Biotechnology Patents.