  Although knowledge bases play an important role in many domains (including in
archives, where they are sometimes used for entity extraction and semantic
annotation tasks), it is challenging to build knowledge bases by hand. This is
owing to a number of factors: Knowledge bases must be accurate, up-to-date,
comprehensive, and as flexible and as efficient as possible. These requirements
mean a large undertaking, in the form of extensive work by subject matter
experts (such as scientists, programmers, archivists, and other information
professionals). Even when successfully engineered, manually built knowledge
bases are typically one-off, use-case-specific, non-standardized,
hard-to-maintain solutions. We present a scalable, flexible, and extensible
architecture for knowledge base construction frameworks. As a work in progress,
we extend a specific framework to address some of its design limitations. The
contributions presented in this short paper can shed a light on the suitability
of using AKBC frameworks for computational use cases in this domain and provide
future directions for building improved AKBC frameworks.
