COVID-19 evolves rapidly and an enormous number of people worldwide desire instant access to COVID-19 information such as the overview, clinic knowledge, vaccine, prevention measures, and COVID-19 mutation. Question a...COVID-19 evolves rapidly and an enormous number of people worldwide desire instant access to COVID-19 information such as the overview, clinic knowledge, vaccine, prevention measures, and COVID-19 mutation. Question answering(QA) has become the mainstream interaction way for users to consume the ever-growing information by posing natural language questions. Therefore, it is urgent and necessary to develop a QA system to offer consulting services all the time to relieve the stress of health services. In particular, people increasingly pay more attention to complex multi-hop questions rather than simple ones during the lasting pandemic, but the existing COVID-19 QA systems fail to meet their complex information needs. In this paper, we introduce a novel multi-hop QA system called COKG-QA, which reasons over multiple relations over large-scale COVID-19 Knowledge Graphs to return answers given a question. In the field of question answering over knowledge graph, current methods usually represent entities and schemas based on some knowledge embedding models and represent questions using pre-trained models. While it is convenient to represent different knowledge(i.e., entities and questions) based on specified embeddings, an issue raises that these separate representations come from heterogeneous vector spaces. We align question embeddings with knowledge embeddings in a common semantic space by a simple but effective embedding projection mechanism. Furthermore, we propose combining entity embeddings with their corresponding schema embeddings which served as important prior knowledge, to help search for the correct answer entity of specified types. In addition, we derive a large multi-hop Chinese COVID-19 dataset(called COKG-DATA for remembering) for COKG-QA based on the linked knowledge graph Open KG-COVID-19 launched by Open KG1, including comprehensive and representative information about COVID-19. COKG-QA achieves quite competitive performance in the 1-hop and 2-hop data while obtaining the best result with significant improvements in the 3-hop. And it is more efficient to be used in the QA system for users. Moreover, the user study shows that the system not only provides accurate and interpretable answers but also is easy to use and comes with smart tips and suggestions.展开更多
Today,constructive journalism continues to develop globally and is flourishing in China.This paper analyzes the distribution of constructive journalism research,media exploration and journalistic practice during the C...Today,constructive journalism continues to develop globally and is flourishing in China.This paper analyzes the distribution of constructive journalism research,media exploration and journalistic practice during the COVID-19 pandemic in China on the basis of tracked observations,research and interviews,and cooperation pilots;outlines the structure and changes of journalism,especially audiovisual journalism,in China from a"constructive"perspective;and provides a comprehensive overview of constructive journalism research and practice in China.It aims at considering the relationship between journalism,the media and society in the new environment and providing reference material for the development of constructive journalism and Sino-Western dialogue.The study finds that constructive journalism is not only an important concept in the development of Chinese journalism,but also a journalistic practice with deep roots.Since the beginning of the 21st century,incremental progress has been achieved,marked by the keywords of people’s livelihood,lending a hand,"questioning officials",and construction.Constructive journalism is becoming a means and a channel for media participation in social governance,as was clearly shown during the COVID-19 pandemic.展开更多
基金supported by the Fundamental Research Funds for the Central Universities with grant Nos.22120220069the National Nature Science Foundation of China with Grant No.62176185supported in part by the Shanghai Artificial Intelligence Innovation and Development Fund grant 2020RGZN-02026
文摘COVID-19 evolves rapidly and an enormous number of people worldwide desire instant access to COVID-19 information such as the overview, clinic knowledge, vaccine, prevention measures, and COVID-19 mutation. Question answering(QA) has become the mainstream interaction way for users to consume the ever-growing information by posing natural language questions. Therefore, it is urgent and necessary to develop a QA system to offer consulting services all the time to relieve the stress of health services. In particular, people increasingly pay more attention to complex multi-hop questions rather than simple ones during the lasting pandemic, but the existing COVID-19 QA systems fail to meet their complex information needs. In this paper, we introduce a novel multi-hop QA system called COKG-QA, which reasons over multiple relations over large-scale COVID-19 Knowledge Graphs to return answers given a question. In the field of question answering over knowledge graph, current methods usually represent entities and schemas based on some knowledge embedding models and represent questions using pre-trained models. While it is convenient to represent different knowledge(i.e., entities and questions) based on specified embeddings, an issue raises that these separate representations come from heterogeneous vector spaces. We align question embeddings with knowledge embeddings in a common semantic space by a simple but effective embedding projection mechanism. Furthermore, we propose combining entity embeddings with their corresponding schema embeddings which served as important prior knowledge, to help search for the correct answer entity of specified types. In addition, we derive a large multi-hop Chinese COVID-19 dataset(called COKG-DATA for remembering) for COKG-QA based on the linked knowledge graph Open KG-COVID-19 launched by Open KG1, including comprehensive and representative information about COVID-19. COKG-QA achieves quite competitive performance in the 1-hop and 2-hop data while obtaining the best result with significant improvements in the 3-hop. And it is more efficient to be used in the QA system for users. Moreover, the user study shows that the system not only provides accurate and interpretable answers but also is easy to use and comes with smart tips and suggestions.
文摘Today,constructive journalism continues to develop globally and is flourishing in China.This paper analyzes the distribution of constructive journalism research,media exploration and journalistic practice during the COVID-19 pandemic in China on the basis of tracked observations,research and interviews,and cooperation pilots;outlines the structure and changes of journalism,especially audiovisual journalism,in China from a"constructive"perspective;and provides a comprehensive overview of constructive journalism research and practice in China.It aims at considering the relationship between journalism,the media and society in the new environment and providing reference material for the development of constructive journalism and Sino-Western dialogue.The study finds that constructive journalism is not only an important concept in the development of Chinese journalism,but also a journalistic practice with deep roots.Since the beginning of the 21st century,incremental progress has been achieved,marked by the keywords of people’s livelihood,lending a hand,"questioning officials",and construction.Constructive journalism is becoming a means and a channel for media participation in social governance,as was clearly shown during the COVID-19 pandemic.
