AUTOMATIC COMPETITOR IDENTIFICATION FROM PUBLIC INFORMATION SOURCES
Dmitry Zelenko, OLEG SEMIN · International Journal of Computational Intelligence and Applications · 2002
We present an automatic system that discovers competing companies from public information sources. The system extracts data from text, uses transformation-based learning to obtain appropriate data normalization, combines structured and unstructured information sources, uses probabilistic modelling to represent models of linked data, and succeeds in autonomously discovering competitors. We also introduce the iterative graph reconstruction process for inference in relational data, and show that it leads to improvements in performance. We validate system results and deploy it on the web as a powerful analytic tool for individual and institutional investors.