Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural
language questions via well-structured relation information between entities stored in …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Recently, a large number of studies focus on semantically or syntactically …

Existing studies on question answering on knowledge bases (KBQA) mainly operate with
the standard iid assumption, ie, training distribution over questions is the same as the test …

Previous work on answering complex questions from knowledge bases usually separately
addresses two types of complexity: questions with constraints and questions with multiple …

Existing KBQA approaches, despite achieving strong performance on iid test data, often
struggle in generalizing to questions involving unseen KB schema items. Prior ranking …

Answering complex natural language questions on knowledge graphs (KGQA) is a
challenging task. It requires reasoning with the input natural language questions as well as …

Knowledge Graph Question Answering aims to automatically answer natural language
questions via well-structured relation information between entities stored in knowledge …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Early studies mainly focused on answering simple questions over KBs and …

Question answering, serving as one of important tasks in natural language processing,
enables machines to understand questions in natural language and answer the questions …