Incremental Compilation of Knowledge Documents for
Markup-based Closed-World Authoring
K-CAP '11: Proceedings of the sixth international conference on Knowledge Capture, ACM, (2011)Abstract
Text-based authoring using knowledge markups is an increasingly
popular editing paradigm in manual knowledge
acquisition. Closed world authoring environments support
the user to form a coherent knowledge base by checking the
referenced objects against a set of declared domain objects.
In this scenario, the task of efficient translation (compilation)
of the text sources is non-trivial. Additionally, in realworld
applications frequent small changes are performed on
the source documents and instant feedback to the author
is crucial. Therefore, a scalable compilation into the target
knowledge representations is necessary. In this paper, we introduce
a general algorithm for the incremental compilation
of knowledge documents, that analyzes the current document
modi cations and performs minimal updates on the
knowledge base. We provide a formal proof of the correctness
of the algorithm and show the e ffectivenes of the approach
in several case studies, using various kinds of knowledge
representations and markups.
