Text mining is an exciting application ?eld and an area of scienti?c - search that is currently under rapid development. It uses techniques from well-established scienti?c ?elds (e. g. data mining, machine learning, infor- tion retrieval, natural language processing, case-based reasoning, statistics and knowledge management) in an e?ort to help people gain insight, und- stand and interpret large quantities of (usually) semi-structured and unstr- tured data. Despite the advances made during the last few years, many issues remain unresolved. Proper co-ordination activities, dissemination of current trends and standardisation of the procedures have been identi?ed, as key needs. There are many questions still unanswered, especially to the potential users; what is the scope of Text Mining, who uses it and for what purpose, what constitutes the leading trends in the ?eld of Text Mining – especially in relation to IT – and whether there still remain areas to be covered. Knowledge Mining draws upon many of the key concepts of knowledge management, data mining and knowledge discovery, meta-analysis and data visualization. Within the context of scienti?c research, knowledge mining is principally concerned with the quantitative synthesis and visualization of - search results and ?ndings. The results of knowledge mining are increased scienti?c understanding along with improvements in research quality and value. Knowledge mining products can be used to highlight research opportunities, assist with the p- sentation of “best” scienti?c evidence, facilitate research portfolio mana- ment, as well as, facilitate policy setting and decision making.
