The task of answering natural language questions over structured data has received wide

interest in recent years. Structured data in the form of knowledge bases has been available

for public usage with coverage on multiple domains. DBpedia and Freebase are such knowledge

bases that include encyclopedic data about multiple domains. However, querying such

knowledge bases requires an understanding of a query language and the underlying ontology,

which requires domain expertise. Querying structured data via question answering systems

that understand natural language has gained popularity to bridge the gap between the data

and the end user.

In order to understand a natural language question, a question answering system needs

to map the question into query representation that can be evaluated given a knowledge base.

An important aspect that we focus in this thesis is the multilinguality. While most research

focused on building monolingual solutions, mainly English, this thesis focuses on building

multilingual question answering systems. The main challenge for processing language input

is interpreting the meaning of questions in multiple languages.

In this thesis, we present three different semantic parsing approaches that learn models

to map questions into meaning representations, into a query in particular, in a supervised

fashion. Each approach differs in the way the model is learned, the features of the model, the

way of representing the meaning and how the meaning of questions is composed. The first

approach learns a joint probabilistic model for syntax and semantics simultaneously from the

labeled data. The second method learns a factorized probabilistic graphical model that builds

on a dependency parse of the input question and predicts the meaning representation that is

converted into a query. The last approach presents a number of different neural architectures

that tackle the task of question answering in end-to-end fashion. We evaluate each approach

using publicly available datasets and compare them with state-of-the-art QA systems.