Relation Inference and Type Identification Based on Brain Knowledge Graph

	Relation Inference and Type Identification Based on Brain Knowledge Graph
	Zhu, Hongyin1 ; Zeng, Yi1,2 ; Wang, Dongsheng1 ; Xu, Bo 1,2
	2016-10
会议名称	2016 International Conference on Brain Informatics and Health
会议日期	October 13-16, 2016
会议地点	Omaha, Nebraska, USA
摘要	Large-scale brain knowledge bases, such as Linked Brain Data, integrate and synthesize domain knowledge on the brain from various data sources. Although it is designed to provide comprehensive understanding of the brain from multiple perspectives and multi-scale, the correctness and specificity of the extracted knowledge is very important. In this paper, we propose a framework of relation inference and relation type identification to solve the upper problem. Firstly, we propose a quadrilateral closure method based on the network topology to verify and infer the binary relations. Secondly, we learn a model based on artificial neural network to predict the potential relations. Finally, we propose a model free method to identify the specific type of relations based on dependency parsing. We test our verified relations on the annotated data, and the result demonstrates a promising performance.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14445
专题	脑图谱与类脑智能实验室_类脑认知计算
通讯作者	Zeng, Yi
作者单位	1.Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhu, Hongyin,Zeng, Yi,Wang, Dongsheng,et al. Relation Inference and Type Identification Based on Brain Knowledge Graph[C],2016.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Relation Inference a（731KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载