  These days, vast amounts of knowledge are available online, most of it in
written form. Search engines help us access this knowledge, but aggregating,
relating and reasoning with it is still a predominantly human effort. One of
the key challenges for automated reasoning based on natural-language texts is
the need to extract meaning (semantics) from texts. Natural language
understanding (NLU) systems describe the conversion from a set of natural
language utterances to terms in a particular logic. Tools for the
co-development of grammar and target logic are currently largely missing.
  We will describe the Grammatical Logical Framework (GLF), a combination of
two existing frameworks, in which large parts of a symbolic, rule-based NLU
system can be developed and implemented: the Grammatical Framework (GF) and
MMT. GF is a tool for syntactic analysis, generation, and translation with
complex natural language grammars and MMT can be used to specify logical
systems and to represent knowledge in them. Combining these tools is possible,
because they are based on compatible logical frameworks: Martin-L\"of type
theory and LF. The flexibility of logical frameworks is needed, as NLU research
has not settled on a particular target logic for meaning representation.
Instead, new logics are developed all the time to handle various language
phenomena. GLF allows users to develop the logic and the language parsing
components in parallel, and to connect them for experimentation with the entire
pipeline.
