UCLA

As the volume of biomedical literature increases, it can be challenging for clinicians to stay up-to-date on this massive store of knowledge. Graphical summarization systems condense knowledge into a more tractable form via "concept maps" -- networks of nodes (concepts) and edges (relations between concepts). In existing graphical summarization systems, the context of the extracted relations (such as study design and study population) is omitted. However, context is crucial for capturing the full meaning of a relation. With context, the user may pose more detailed queries than those accommodated by traditional, context-free maps.

This dissertation describes Casama, a system for creating "contextualized semantic maps" to represent the current state of scientific knowledge in the domain of non-small cell lung cancer (NSCLC). A formalism for contextualized semantic maps is presented, including targeted relations, study design context, and study population context. An annotated gold standard conforming to this representation is produced, and methods for extracting these contexts are developed. Contextualized semantic maps are evaluated in an information retrieval task and a summarization usability study, showing significant improvement over PubMed and SemRep.