Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of
them becomes increasingly important. Question answering over knowledge base (KB-QA) is …

We provide a comprehensive survey of the research literature that applies Information
Extraction techniques in a Semantic Web setting. Works in the intersection of these two …

The rise of personal assistants has made conversational question answering (ConvQA) a
very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA …

Question answering over knowledge graphs (KGQA) has evolved from simple single-fact
questions to complex questions that require graph traversal and aggregation. We propose a …

Answering complex questions is one of the challenges that question-answering (QA)
systems face today. While complexity has several facets, question dimensions like temporal …

By reason of being able to obtain natural language responses, natural answers are more
favored in real-world Question Answering (QA) systems. Generative models learn to …

Direct answering of questions that involve multiple entities and relations is a challenge for
text-based QA. This problem is most pronounced when answers can be found only by …

This chapter discusses the abstract concepts necessary to realise a Web of Data. We
discuss how the content on the Web can be represented as graph-structured data in order to …

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of
them becomes increasingly important. Knowledge base-based question answering (KB-QA) …