Improving Question Answering over Knowledge Graphs Using Graph Summarization
Sirui Li, Kok Kai Wong, Dengya Zhu, Chun Che Fung

TL;DR
This paper introduces a novel graph summarization method combining RCNN and GCN to improve question answering over knowledge graphs, especially for questions with uncertain answer counts, outperforming previous GCN-based approaches.
Contribution
The paper proposes a new graph summarization technique using RCNN and GCN that enhances entity embeddings and answer recall in KGQA systems for single-relation questions.
Findings
Significant improvement in answer recall for uncertain answer questions
Better entity embeddings through combined RCNN and GCN approach
Outperforms standard GCN-based methods in experiments
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Advanced Graph Neural Networks
MethodsGraph Convolutional Network
