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The task of answering natural language questions over RDF data has receivedwide interest in recent years, in particular in the context of the series ofQALD benchmarks. The task consists of mapping a natural language question to anexecutable 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 ofhard-coded rules to interpret questions and map them into a SPARQL query. Wepresent the first multilingual QALD pipeline that induces a model from trainingdata for mapping a natural language question into logical form as probabilisticinference. In particular, our approach learns to map universal syntacticdependency representations to a language-independent logical form based onDUDES (Dependency-based Underspecified Discourse Representation Structures)that are then mapped to a SPARQL query as a deterministic second step. Ourmodel builds on factor graphs that rely on features extracted from thedependency graph and corresponding semantic representations. We rely onapproximate inference techniques, Markov Chain Monte Carlo methods inparticular, as well as Sample Rank to update parameters using a rankingobjective. Our focus lies on developing methods that overcome the lexical gapand present a novel combination of machine translation and word embeddingapproaches for this purpose. As a proof of concept for our approach, weevaluate our approach on the QALD-6 datasets for English, German & Spanish.

AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data