  With an increase in Geospatial Linked Open Data being adopted and published
over the web, there is a need to develop intuitive interfaces and systems for
seamless and efficient exploratory analysis of such rich heterogeneous
multi-modal datasets. This work is geared towards improving the exploration
process of Earth Observation (EO) Linked Data by developing a natural language
interface to facilitate querying. Questions asked over Earth Observation Linked
Data have an inherent spatio-temporal dimension and can be represented using
GeoSPARQL. This paper seeks to study and analyze the use of RNN-based neural
machine translation with attention for transforming natural language questions
into GeoSPARQL queries. Specifically, it aims to assess the feasibility of a
neural approach for identifying and mapping spatial predicates in natural
language to GeoSPARQL's topology vocabulary extension including - Egenhofer and
RCC8 relations. The queries can then be executed over a triple store to yield
answers for the natural language questions. A dataset consisting of mappings
from natural language questions to GeoSPARQL queries over the Corine Land
Cover(CLC) Linked Data has been created to train and validate the deep neural
network. From our experiments, it is evident that neural machine translation
with attention is a promising approach for the task of translating spatial
predicates in natural language questions to GeoSPARQL queries.
