Acquisition of Generic Problem Solving Knowledge through Information Extraction and Pattern Mining

In many technical domains, the generic problem-solving knowledge is scarce even hough a large number of concrete resolutions exist and are well documented. This makes the machine learning from resolution traces approach facing a number of challenges, not least among them the complexity of the underlying domain (concepts, relationships, events, processes, etc.) and the machine-readability of the documented resolution. We tackle here the acquisition of expertise in phylogeny, which is a notoriously rich and prolific field where hundreds, if not thousands, concrete cases are reported in the literature, yet tools to assist the phylogenist in analyzing a new dataset are virtually absent. Thus, we propose an approach that amounts to ontology-based workflow mining: Our T-GROWLer system abstracts general patterns from event sequences previously extracted from texts. It comprises two modules -- a workflow extractor and a pattern miner -- both relying on a pair of ontologies (a domain one and a procedural one).