Automatic organization of human task goals for web-scale problem solving knowledge

Problem solving knowledge is omnipresent and scattered on the Web. While extracting and gathering such knowledge has been a focus of attention, it is equally important to devise a way to organize such knowledge for both human and machine consumption with respect to task goals. As a way to provide an extensive knowledge structure for human task goals, with which human problem solving knowledge extracted from Web resources can be organized, we devised a method for automatically grouping and organizing the goal statements in a Web 2.0 site that contains over two millions how-to instruction articles covering almost all task domains. In the proposed method, task goals having semantically and task-categorically similar action types and object types are grouped together by analyzing predicate-argument association patterns across all the goal statements through bipartite EM-like modeling. The result obtained with the unsupervised machine learning algorithm was evaluated by means of a human-annotated data set in a sample domain.