Improving Question Answering over Knowledge Graphs using Graph Summarization
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 Kno...
| Main Authors: | , , , |
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| Format: | Conference Paper |
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Springer
2021
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| Online Access: | https://link.springer.com/chapter/10.1007/978-3-030-92273-3_40 http://hdl.handle.net/20.500.11937/87131 |
| _version_ | 1848764898557820928 |
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| author | Li, Sirui Wong, Kok Wai Fung, Chun Che Zhu, Dengya |
| author_facet | Li, Sirui Wong, Kok Wai Fung, Chun Che Zhu, Dengya |
| author_sort | Li, Sirui |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:26:40Z |
| format | Conference Paper |
| id | curtin-20.500.11937-87131 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:26:40Z |
| publishDate | 2021 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-871312022-01-11T05:55:38Z Improving Question Answering over Knowledge Graphs using Graph Summarization Li, Sirui Wong, Kok Wai Fung, Chun Che Zhu, Dengya 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. 2021 Conference Paper http://hdl.handle.net/20.500.11937/87131 https://link.springer.com/chapter/10.1007/978-3-030-92273-3_40 Springer restricted |
| spellingShingle | Li, Sirui Wong, Kok Wai Fung, Chun Che Zhu, Dengya Improving Question Answering over Knowledge Graphs using Graph Summarization |
| title | Improving Question Answering over Knowledge Graphs using Graph Summarization |
| title_full | Improving Question Answering over Knowledge Graphs using Graph Summarization |
| title_fullStr | Improving Question Answering over Knowledge Graphs using Graph Summarization |
| title_full_unstemmed | Improving Question Answering over Knowledge Graphs using Graph Summarization |
| title_short | Improving Question Answering over Knowledge Graphs using Graph Summarization |
| title_sort | improving question answering over knowledge graphs using graph summarization |
| url | https://link.springer.com/chapter/10.1007/978-3-030-92273-3_40 http://hdl.handle.net/20.500.11937/87131 |