  Narrative sensemaking is an essential part of understanding sequential data.
Narrative maps are a visual representation model that can assist analysts to
understand narratives. In this work, we present a semantic interaction (SI)
framework for narrative maps that can support analysts through their
sensemaking process. In contrast to traditional SI systems which rely on
dimensionality reduction and work on a projection space, our approach has an
additional abstraction layer -- the structure space -- that builds upon the
projection space and encodes the narrative in a discrete structure. This extra
layer introduces additional challenges that must be addressed when integrating
SI with the narrative extraction pipeline. We address these challenges by
presenting the general concept of Mixed Multi-Model Semantic Interaction (3MSI)
-- an SI pipeline, where the highest-level model corresponds to an abstract
discrete structure and the lower-level models are continuous. To evaluate the
performance of our 3MSI models for narrative maps, we present a quantitative
simulation-based evaluation and a qualitative evaluation with case studies and
expert feedback. We find that our SI system can model the analysts' intent and
support incremental formalism for narrative maps.
