  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 providing entity embeddings. However, using GCNs to
KGQAs is inefficient because GCNs treat all relations equally when aggregating
neighbourhoods. Also, a problem could occur when using previous KGQAs: in most
cases, questions often have an uncertain number of answers. To address the
above issues, we propose a graph summarization technique using Recurrent
Convolutional Neural Network (RCNN) and GCN. The combination of GCN and RCNN
ensures that the embeddings are propagated together with the relations relevant
to the question, and thus better answers. The proposed graph summarization
technique can be used to tackle the issue that KGQAs cannot answer questions
with an uncertain number of answers. In this paper, we demonstrated the
proposed technique on the most common type of questions, which is
single-relation questions. Experiments have demonstrated that the proposed
graph summarization technique using RCNN and GCN can provide better results
when compared to the GCN. The proposed graph summarization technique
significantly improves the recall of actual answers when the questions have an
uncertain number of answers.
