Knowledge Discovery from Natural Languages: a Linguistic Dataset of 10K Kinship Relations

doi:10.1109/BDAI59165.2023.10257043

	Knowledge Discovery from Natural Languages: a Linguistic Dataset of 10K Kinship Relations
	Yue Yangming 1; Li Chunxiao 1; Chen YeZeng2 ; Dai Zijie 1; Zhou Yi 1
	2023-07-07
会议录名称	2023 IEEE 6TH INTERNATIONAL CONFERENCE ON BIG DATA AND ARTIFICIAL INTELLIGENCE (BDAI)
页码	79-88
发表状态	已发表
DOI	10.1109/BDAI59165.2023.10257043
摘要	In the expansive realm of knowledge discovery, this study propels forward the subdomain of rule mining with the inception of a singular synthetic dataset - the Kinship 10K Dataset. This dataset, purpose-built for natural language rule mining, derives from the intricate relationship networks across 20 simulated families. These networks include 1,500 unique characters. The development leverages generative techniques, producing a rich array of kinship rules. Each rule is grounded in one of eight foundational Meta kinship relations. The final ensemble, a comprehensive dataset, comprises 10,526 relationship instances, 234 distinct kinship relations, and 104 learnable rules. In addition, we introduce two evaluation metrics - Rule Coverage (RC) and Directed Rule Mining Capability (DRMC) for examining rule mining algorithms in closed domains. RC quantifies the inclusiveness of rule mining datasets, while DRMC delivers nuanced analysis of algorithmic performance in discerning and extracting precise rules, taking accuracy and precision into account. Additionally, we set a benchmark by utilizing the GPT-3.5 and GPT-4 models as baselines. It is noteworthy that the GPT-4 model attained scores of 0.78 and 0.35 on the RC and DRMC metrics respectively. These scores underscore the inherent challenges of the task and signify the merit in pursuing further research to advance this domain. Collectively, this investigation presents a substantial contribution to knowledge discovery. By introducing an innovative dataset, formulating novel evaluation metrics, and instituting a robust baseline model, it not only highlights the prospects for deeper insights and increased automation in the wider field of knowledge discovery but also sets the stage for upcoming advancements in rule mining research. © 2023 IEEE.
会议录编者/会议主办者	Yangtze Delta Region Institute of Tsinghua University Zhejiang
关键词	Knowledge Discovery Rule Mining Natural language Kinship Dataset
会议名称	6th International Conference on Big Data and Artificial Intelligence, BDAI 2023
会议地点	Jiaxing, China
会议日期	7-9 July 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20234515019957
EI主题词	Data mining
EI分类号	723.2 Data Processing and Image Processing
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/333541
专题	信息科学与技术学院_硕士生
作者单位	1.University of Science and Technology of China, Hefei, China 2.Shanghai Innovation Center for Processor Technologies, ShanghaiTech University, Shanghai, China
推荐引用方式 GB/T 7714	Yue Yangming,Li Chunxiao,Chen YeZeng,et al. Knowledge Discovery from Natural Languages: a Linguistic Dataset of 10K Kinship Relations[C]//Yangtze Delta Region Institute of Tsinghua University Zhejiang:Institute of Electrical and Electronics Engineers Inc.,2023:79-88.