  Spatial Reasoning from language is essential for natural language
understanding. Supporting it requires a representation scheme that can capture
spatial phenomena encountered in language as well as in images and videos.
Existing spatial representations are not sufficient for describing spatial
configurations used in complex tasks. This paper extends the capabilities of
existing spatial representation languages and increases coverage of the
semantic aspects that are needed to ground the spatial meaning of natural
language text in the world. Our spatial relation language is able to represent
a large, comprehensive set of spatial concepts crucial for reasoning and is
designed to support the composition of static and dynamic spatial
configurations. We integrate this language with the Abstract Meaning
Representation(AMR) annotation schema and present a corpus annotated by this
extended AMR. To exhibit the applicability of our representation scheme, we
annotate text taken from diverse datasets and show how we extend the
capabilities of existing spatial representation languages with the fine-grained
decomposition of semantics and blend it seamlessly with AMRs of sentences and
discourse representations as a whole.
