Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture
The domestic population has paid increasing attention to ancient Chinese history and culture with the continuous improvement of people’s living standards, the rapid economic growth, and the rapid advancement of information science and technology. The use of information technology has been proven to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/11/4/186 |
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| author | Shuang Liu Hui Yang Jiayi Li Simon Kolmanič |
| author_facet | Shuang Liu Hui Yang Jiayi Li Simon Kolmanič |
| author_sort | Shuang Liu |
| collection | DOAJ |
| description | The domestic population has paid increasing attention to ancient Chinese history and culture with the continuous improvement of people’s living standards, the rapid economic growth, and the rapid advancement of information science and technology. The use of information technology has been proven to promote the spread and development of historical culture, and it is becoming a necessary means to promote our traditional culture. This paper will build a knowledge graph of ancient Chinese history and culture in order to facilitate the public to more quickly and accurately understand the relevant knowledge of ancient Chinese history and culture. The construction process is as follows: firstly, use crawler technology to obtain text and table data related to ancient history and culture on Baidu Encyclopedia (similar to Wikipedia) and ancient Chinese history and culture related pages. Among them, the crawler technology crawls the semi-structured data in the information box (InfoBox) in the Baidu Encyclopedia to directly construct the triples required for the knowledge graph, crawls the introductory text information of the entries in Baidu Encyclopedia, and specialized historical and cultural websites (history Chunqiu.com, On History.com) to extract unstructured entities and relationships. Secondly, entity recognition and relationship extraction are performed on an unstructured text. The entity recognition part uses the Bidirectional Long Short-Term Memory-Convolutional Neural Networks-Conditions Random Field (BiLSTM-CNN-CRF) model for entity extraction. The relationship extraction between entities is performed by using the open source tool DeepKE (information extraction tool with language recognition ability developed by Zhejiang University) to extract the relationships between entities. After obtaining the entity and the relationship between the entities, supplement it with the triple data that were constructed from the semi-structured data in the existing knowledge base and Baidu Encyclopedia information box. Subsequently, the ontology construction and the quality evaluation of the entire constructed knowledge graph are performed to form the final knowledge graph of ancient Chinese history and culture. |
| first_indexed | 2024-03-10T20:48:25Z |
| format | Article |
| id | doaj.art-ff50a69e0d9a4b9695994e7fb6de1fee |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T20:48:25Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-ff50a69e0d9a4b9695994e7fb6de1fee2023-11-19T20:11:44ZengMDPI AGInformation2078-24892020-03-0111418610.3390/info11040186Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and CultureShuang Liu0Hui Yang1Jiayi Li2Simon Kolmanič3School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, ChinaSchool of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, ChinaSchool of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, ChinaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroska cesta 46, SI-2000 Maribor, SloveniaThe domestic population has paid increasing attention to ancient Chinese history and culture with the continuous improvement of people’s living standards, the rapid economic growth, and the rapid advancement of information science and technology. The use of information technology has been proven to promote the spread and development of historical culture, and it is becoming a necessary means to promote our traditional culture. This paper will build a knowledge graph of ancient Chinese history and culture in order to facilitate the public to more quickly and accurately understand the relevant knowledge of ancient Chinese history and culture. The construction process is as follows: firstly, use crawler technology to obtain text and table data related to ancient history and culture on Baidu Encyclopedia (similar to Wikipedia) and ancient Chinese history and culture related pages. Among them, the crawler technology crawls the semi-structured data in the information box (InfoBox) in the Baidu Encyclopedia to directly construct the triples required for the knowledge graph, crawls the introductory text information of the entries in Baidu Encyclopedia, and specialized historical and cultural websites (history Chunqiu.com, On History.com) to extract unstructured entities and relationships. Secondly, entity recognition and relationship extraction are performed on an unstructured text. The entity recognition part uses the Bidirectional Long Short-Term Memory-Convolutional Neural Networks-Conditions Random Field (BiLSTM-CNN-CRF) model for entity extraction. The relationship extraction between entities is performed by using the open source tool DeepKE (information extraction tool with language recognition ability developed by Zhejiang University) to extract the relationships between entities. After obtaining the entity and the relationship between the entities, supplement it with the triple data that were constructed from the semi-structured data in the existing knowledge base and Baidu Encyclopedia information box. Subsequently, the ontology construction and the quality evaluation of the entire constructed knowledge graph are performed to form the final knowledge graph of ancient Chinese history and culture.https://www.mdpi.com/2078-2489/11/4/186knowledge graphancient history and cultureknowledge extractionnamed entity recognitionvisual display |
| spellingShingle | Shuang Liu Hui Yang Jiayi Li Simon Kolmanič Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture Information knowledge graph ancient history and culture knowledge extraction named entity recognition visual display |
| title | Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture |
| title_full | Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture |
| title_fullStr | Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture |
| title_full_unstemmed | Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture |
| title_short | Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture |
| title_sort | preliminary study on the knowledge graph construction of chinese ancient history and culture |
| topic | knowledge graph ancient history and culture knowledge extraction named entity recognition visual display |
| url | https://www.mdpi.com/2078-2489/11/4/186 |
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