Text mining for creative cross-domain knowledge discovery
Abstract
Understanding complex phenomena and solving difficult problems often requires knowledge from different domains to be combined and cross-domain associations to be taken into account. These kinds of context-crossing associations are called bisociations (Koestler, 1964) and are often needed for creative, innovative discoveries. The ability of text mining methods and software tools for supporting the experts in the knowledge discovery process, especially in searching for yet unexplored connections between different domains, is becoming increasingly challenging, due to over-specialization of research areas, resulting in islands of deep, but relatively isolated knowledge. This talk will present an exploration engine for text mining and cross-context link discovery, CrossBee, implemented as a Web application with a user-friendly interface. The system supports experts in advanced document exploration by facilitating document retrieval, analysis and visualization. It enables document retrieval from public databases like PubMed, as well as by querying the Web, followed by document cleaning and filtering through several filtering criteria.
Short bio of Prof. Nada Lavrač
Nada Lavrač is Head of Department of Knowledge Technologies of Jozef Stefan Institute, Ljubljana, Slovenia. She is also Professor at Jozef Stefan International Postgraduate School in Ljubljana and at University of Nova Gorica. Her main research interests are in Knowledge Technologies, with particular interests in machine learning, data mining, text mining, knowledge management and computational creativity. Areas of applied research include data mining applications in medicine, health care and bioinformatics, media analysis and virtual enterprises.