The task of answering natural language questions over RDF data has

received wIde interest in recent years, in particular in the context of the series

of QALD benchmarks. The task consists of mapping a natural language question

to an executable form, e.g. SPARQL, so that answers from a given KB can

be extracted. So far, most systems proposed are i) monolingual and ii) rely on

a set of hard-coded rules to interpret questions and map them into a SPARQL

query. We present the first multilingual QALD pipeline that induces a model

from training data for mapping a natural language question into logical form as

probabilistic inference. In particular, our approach learns to map universal syntactic

dependency representations to a language-independent logical form based

on DUDES (Dependency-based Underspecified Discourse Representation Structures)

that are then mapped to a SPARQL query as a deterministic second step.

Our model builds on factor graphs that rely on features extracted from the dependency

graph and corresponding semantic representations.We rely on approximate

inference techniques, Markov Chain Monte Carlo methods in particular, as well

as Sample Rank to update parameters using a ranking objective. Our focus lies on

developing methods that overcome the lexical gap and present a novel combination

of machine translation and word embedding approaches for this purpose. As

a proof of concept for our approach, we evaluate our approach on the QALD-6

datasets for English, German & Spanish.