Computer-aided causal knowledge discovery from text documents of process and equipment malfunctions

The importance of recording data of process and equipment malfunctions has been widely addressed and an enormous quantity of collections now exists. W ith problems regarding confidentiality being gradually solved and now with widespread use of computers, the need to develop computer-aided systems to help digest the large volumes of data is critical. Work so far has mainly c oncentrated on developing systems to help retrieval. Besides retrieval, the current approaches are not able to indicate further uses of the data. In t his contribution, a prototype system is described that can be used interact ively to discover causal knowledge from collections of text documents about process malfunctions and represent the knowledge in terms of rules and gra phical models. This is a flexible system that gives users the freedom to co ntrol the knowledge discovery process, while at the same time providing use rs with maximum support of automatic functions. The system is also featured with a learning mechanism that allows continuous improvement of system per formance during use. The graphical networks can also be used to retrieve th e original text records from which a causal relationship is extracted.