Relation Inference and Type Identification Based on Brain Knowledge Graph
文献类型：会议论文
| 作者 | Zhu, Hongyin1 ; Zeng, Yi1,2; Wang, Dongsheng1; Xu, Bo1,2
|
| 出版日期 | 2016-10 |
| 会议日期 | October 13-16, 2016 |
| 会议地点 | Omaha, Nebraska, USA |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14445]  |
| 专题 | 类脑智能研究中心_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 |
| 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]. 见:. Omaha, Nebraska, USA. October 13-16, 2016. |
入库方式： OAI收割
来源：自动化研究所
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