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
Conference Name2016 International Conference on Brain Informatics and Health
Conference DateOctober 13-16, 2016
Conference PlaceOmaha, Nebraska, USA
AbstractLarge-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.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14445
Collection类脑智能研究中心
Corresponding AuthorZeng, Yi
Affiliation1.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
Recommended Citation
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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