  Conversational interfaces that allow for intuitive and comprehensive access
to digitally stored information remain an ambitious goal. In this thesis, we
lay foundations for designing conversational search systems by analyzing the
requirements and proposing concrete solutions for automating some of the basic
components and tasks that such systems should support. We describe several
interdependent studies that were conducted to analyse the design requirements
for more advanced conversational search systems able to support complex
human-like dialogue interactions and provide access to vast knowledge
repositories. In the first two research chapters, we focus on analyzing the
structures common to information-seeking dialogues by capturing recurrent
patterns in terms of both domain-independent functional relations between
utterances as well as domain-specific implicit semantic relations from shared
background knowledge.
  Our results show that question answering is one of the key components
required for efficient information access but it is not the only type of
dialogue interactions that a conversational search system should support. In
the third research chapter, we propose a novel approach for complex question
answering from a knowledge graph that surpasses the current state-of-the-art
results in terms of both efficacy and efficiency. In the last research chapter,
we turn our attention towards an alternative interaction mode, which we termed
conversational browsing, in which, unlike question answering, the
conversational system plays a more pro-active role in the course of a dialogue
interaction. We show that this approach helps users to discover relevant items
that are difficult to retrieve using only question answering due to the
vocabulary mismatch problem.
