CASIA OpenIR  > 类脑智能研究中心
Relation Inference and Type Identification Based on Brain Knowledge Graph
Zhu, Hongyin1; Zeng, Yi1,2; Wang, Dongsheng1; Xu, Bo1,2
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
专题类脑智能研究中心
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
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.
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