Domain knowledge acquisition and plan recognition by probabilistic reasoning

In this paper, a probabilistic framework for acquiring domain knowledge from heterogeneous corpora is introduced. The acquired information is used for intelligent human-computer interaction through the web. The application selected for the framework experimentation was education on issues of chemotherapy of nosocomial and community acquired pneumonia. Contrasting to existing educational dialogue engines which use handcrafted knowledge of the application domain, our approach utilizes automatic encoding of the semantic model of the application, based on learning Bayesian networks from past user questions. The structure of the networks as well as the conditional probability distributions are computed automatically from dialogue corpora, thus eliminating the tedious process of manual insertion of domain knowledge. Furthermore, we attempt to overcome the significant issue of limited vocabulary by incorporating a methodology which estimates semantic similarities of words not found within the systems vocabulary and probabilistically associates them with those who appear.