Humans are able to apply abstract reasoning patterns to learned knowledge and principles, and infer correct results. Our work is motivated by the vision of automated asking and answering of questions related to a biology textbook - a capability which requires application of abstract reasoning patterns. For example, biological processes such as cellular respiration have an intricate structure that defines the ordering among different steps and the participants in each step. A person who understands the process is expected to be able to reason with how the process is affected if one or more steps are interrupted. In this article we analyze a family of questions about process interruption, and present reasoning patterns that an automated reasoning system can use to answer them. Our reasoning patterns rely on the order of steps of the process and the participants of those steps. We suggest that this approach leads to more intuitive and simpler reasoning than an approach of based on theory of intentions, or an approach that relies on qualitative simulation. Our work is a step toward a system that can discuss answers to questions and assist human learners of biology.

Background: Using knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms? Results: Our existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important. Conclusions: With some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels.

Our aim is for intelligent tutoring agents to replace traditional and even online textbooks with personalized, adaptive, one-to-one instruction. We focus on science subjects, and describe an approach to answering hypothetical questions from the student, such as "Would cellular respiration continue in the absence of oxygen?"