This paper describes R3 ( Reading, Reasoning, and Report- ing ), our system for deep language understanding and model management for the biomedical domain. Starting from a base BioPAX model, we learn extensions to it by reading biomedical research articles from PubMed Central. We describe the particular issues for text understanding in this domain and how we use pre- and post-analysis reasoning to bridge the differences in how knowledge is packaged in a text and in a biomedical database. We close with brief description of our first year results, where R3 was faster than all other reported systems, reading 1,000 articles in 15 minutes.

Despite our increasing understanding of the structure and dynamics of scientific domains, functional knowledge and functional language— such as referring to a central purpose or function of a molecule— permeate scientific articles. Cognitive systems that collaborate with scientists must therefore represent functional knowledge to support machine reading and explanation. This paper describes our progress on automatically inferring and representing functional knowledge in R3 (Reading, Reasoning, and Reporting). R3 automatically reads biology articles from PubMed Central, using a massive domain model from Pathway Commons (www.pathwaycommons.org/) as background knowledge. R3 now relates functional language to its background structural model and explains functional knowledge, which is the central contribution of this paper. We motivate the representation of functional knowledge in the biology domain— which many existing ontologies omit— using examples from PubMed articles. We then describe how R3 automatically adds functional knowledge to its model by parsing textual summaries of biological processes and extracting semantics. We then describe how R3 builds event structures and compositional models with functional knowledge, and we illustrate how R3 uses its functional knowledge to diagram protein activity from the information it learned from reading.

This paper describes an automated process of active perception for cyber defense. Our approach is informed by theoretical ideas from decision theory and recent research results in neuroscience. Our cognitive agent allocates computational and sensing resources to (approximately) optimize its Value of Information. To do this, it draws on models to direct sensors towards phenomena of greatest interest to inform decisions about cyber defense actions. By identifying critical network assets, the organization's mission measures interest (and value of information). This model enables the system to follow leads from inexpensive, inaccurate alerts with targeted use of expensive, accurate sensors. This allows the deployment of sensors to build structured interpretations of situations. From these, an organization can meet mission-centered decision-making requirements with calibrated responses proportional to the likelihood of true detection and degree of threat.