The lessons learned from solving past problems (e.g. how to protect oneself from a disease, how to control the plant disease) and gaining valuable information from previous experience (e.g. disease diagnosis) and the history of disease recurrences (e.g. disease outbreaks) are invaluable for guaranteeing food safety and human health. To reduce the time that users take to learn from such information, a salient information space with semantic link should be developed.
Problem-solving is an intelligent behavior (Kennedy J., et al., 2001) where the goal is to find a solution which satisfies certain criteria. It requires abductive reasoning whereby we apply deductive reasoning in combination with natural language processing. In classical applications as well as in expert systems, abductive inference (Shohei K., et al., 2003) is a complex problem (creation and maintenance) as it is simulated by deductive procedures or rules. On the other hand, qualitative reasoning concerns modeling and inference techniques where continuous phenomena are discretized into a finite number of qualitative categories. In this chapter, language engineering is described as a tool for extracting knowledge represented in unstructured format. The extracted knowledge will be a finite number of specific answers to queries on topics “cause-and-effect “such as disease and symptoms., “problem-and -how-to-do” such disease prevention or control, and biomedicine preparation.