Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Question answering (QA) systems provide a way of querying the information available in
various formats including, but not limited to, unstructured and structured data in natural …

The problem of answering questions using knowledge from pre-trained language models
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …

Abstract Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes
and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) …

Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge
graph (KG) to answer natural language questions. It helps end users more efficiently and …

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 …

Answering complex questions is a time-consuming activity for humans that requires
reasoning and integration of information. Recent work on reading comprehension made …

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

Relation detection is a core component for many NLP applications including Knowledge
Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural …

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 …