Abstract Knowledge graphs are large-scale semantic networks that considerably impact
knowledge representation. Mining hidden knowledge from existing data, including triplet …

Question answering over knowledge bases (KBs) aims to answer natural language
questions with factual information such as entities and relations in KBs. Previous methods …

Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex
questions using knowledge graphs. Currently, KGQA systems achieve great success in …

Answering open-domain questions requires world knowledge about in-context entities. As
pre-trained Language Models (LMs) lack the power to store all required knowledge, external …

We present UniBind a flexible and efficient approach that learns a unified representation
space for seven diverse modalities--images text audio point cloud thermal video and event …

Abstract Knowledge Graph Question Answering (KGQA) aims to answer user-questions from
a knowledge graph (KG) by identifying the reasoning relations between topic entity and …

Large language models have achieved outstanding performance on various downstream
tasks with their advanced understanding of natural language and zero-shot capability …

Inductive reasoning is a core component of human intelligence. In the past research of
inductive reasoning within computer science, formal language is used as representations of …

Knowledge Base Question Answering (KBQA) aims to answer natural language questions
with factual information such as entities and relations in KBs. However, traditional Pre …

Abstract Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves
retrieving nodes from a knowledge graph (KG) to answer natural language questions …