We hypothesize that there are significant synergies between the applications of biomedical ontologies and of biomedical language processing (BLP) which can be used to improve the quality and scope of both activities. A growing body of work suggests such synergies might exist, but there has yet to be a systematic exploration of their potential. We propose to carry out a focused effort to explore both the potential for, and obstacles to, the mutual application of biomedical ontologies and biomedical language processing. To provide immediate biological relevance to our work, we propose to focus on the topics of autoimmune and pulmonary disease. We group our proposed explorations into three specific aims: (1) Create novel tools and approaches for the application and maintenance of biomedical ontologies, based on an assessment of the processes and tools used for the ontological annotation of textual corpora in the biomedical language processing community. Particularly, we will focus on the creation of new methods for effective search through large ontologies, compositional approaches to annotation, effective capture of the evidence underlying annotations, and the use of automated suggestions for manual confirmation. (2) Evaluate the utility of BLP tools and techniques when applied to terms and definitions of biomedical ontologies, both to enrich and interconnect orthogonal ontologies, and to provide quality assurance and quality control mechanisms. Particularly, we propose to develop and evaluate methods for connecting terms within and across ontologies, for assessing completeness of an ontology against the literature, and for implementing automatically executable measures of ontology quality. (3) Compare the differences between annotations produced by manual procedures and those produced by automated BLP methods for completeness and correctness. Based on the resulting data, produce guidelines for the optimal interplay between manual and automatic procedures for producing broad, accurate and useful knowledge-bases. Because ontologies are the central organizing tool of the model organism databases, improvements in their quality and in the ease and efficiency of their use will have a major effect on the model organism databases, speed the translational process generally, and create a potentially large public health impact.