Improving Question Answering over Knowledge Graphs Using Graph Summarization. (arXiv:2203.13570v1 [cs.LG])

Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA)
automatically answer natural language questions using triples contained in a
KG. The key idea is to represent questions and entities of a KG as
low-dimensional embeddings. Previous KGQAs have attempted to represent entities
using Knowledge Graph Embedding (KGE) and Deep Learning (DL) methods. However,
KGEs are too shallow to capture the expressive features and DL methods process
each triple independently. Recently, Graph Convolutional Network (GCN) has
shown to be excellent in …

arxiv graph graphs knowledge knowledge graphs question answering summarization