TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph
Temporal knowledge graphs can be used to represent the current state of the world and, as daily events happen, the need to update the temporal knowledge graph, in order to stay consistent with the state of the world, becomes very important. However, there is currently no reliable method to accuratel...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/8/3/61 |
| _version_ | 1797612508857499648 |
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| author | Ryan Ong Jiahao Sun Ovidiu Șerban Yi-Ke Guo |
| author_facet | Ryan Ong Jiahao Sun Ovidiu Șerban Yi-Ke Guo |
| author_sort | Ryan Ong |
| collection | DOAJ |
| description | Temporal knowledge graphs can be used to represent the current state of the world and, as daily events happen, the need to update the temporal knowledge graph, in order to stay consistent with the state of the world, becomes very important. However, there is currently no reliable method to accurately validate the update and evolution of knowledge graphs. There has been a recent development in text summarisation, whereby question answering is used to both guide and fact-check summarisation quality. The exact process can be applied to the temporal knowledge graph update process. To the best of our knowledge, there is currently no dataset that connects temporal knowledge graphs with documents with question–answer pairs. In this paper, we proposed the TKGQA dataset, consisting of over 5000 financial news documents related to M&A. Each document has extracted facts, question–answer pairs, and before and after temporal knowledge graphs, to highlight the state of temporal knowledge and any changes caused by the facts extracted from the document. As we parse through each document, we use question–answering to check and guide the update process of the temporal knowledge graph. |
| first_indexed | 2024-03-11T06:42:13Z |
| format | Article |
| id | doaj.art-d4c4146faa1a429297d6cf3837fcace7 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-11T06:42:13Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-d4c4146faa1a429297d6cf3837fcace72023-11-17T10:32:07ZengMDPI AGData2306-57292023-03-01836110.3390/data8030061TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge GraphRyan Ong0Jiahao Sun1Ovidiu Șerban2Yi-Ke Guo3Department of Engineering, Imperial College London, London SW7 2BX, UKDepartment of Engineering, Imperial College London, London SW7 2BX, UKDepartment of Engineering, Imperial College London, London SW7 2BX, UKDepartment of Engineering, Imperial College London, London SW7 2BX, UKTemporal knowledge graphs can be used to represent the current state of the world and, as daily events happen, the need to update the temporal knowledge graph, in order to stay consistent with the state of the world, becomes very important. However, there is currently no reliable method to accurately validate the update and evolution of knowledge graphs. There has been a recent development in text summarisation, whereby question answering is used to both guide and fact-check summarisation quality. The exact process can be applied to the temporal knowledge graph update process. To the best of our knowledge, there is currently no dataset that connects temporal knowledge graphs with documents with question–answer pairs. In this paper, we proposed the TKGQA dataset, consisting of over 5000 financial news documents related to M&A. Each document has extracted facts, question–answer pairs, and before and after temporal knowledge graphs, to highlight the state of temporal knowledge and any changes caused by the facts extracted from the document. As we parse through each document, we use question–answering to check and guide the update process of the temporal knowledge graph.https://www.mdpi.com/2306-5729/8/3/61temporal knowledge graphquestion–answeringknowledge graphentity dynamicevent knowledge graphmergers and acquisitions |
| spellingShingle | Ryan Ong Jiahao Sun Ovidiu Șerban Yi-Ke Guo TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph Data temporal knowledge graph question–answering knowledge graph entity dynamic event knowledge graph mergers and acquisitions |
| title | TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph |
| title_full | TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph |
| title_fullStr | TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph |
| title_full_unstemmed | TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph |
| title_short | TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph |
| title_sort | tkgqa dataset using question answering to guide and validate the evolution of temporal knowledge graph |
| topic | temporal knowledge graph question–answering knowledge graph entity dynamic event knowledge graph mergers and acquisitions |
| url | https://www.mdpi.com/2306-5729/8/3/61 |
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