Knowledge Mining

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.