The exponential growth of neuroscience literature presents a significant challenge for researchers seeking to efficiently access and utilize relevant information. To address this issue, we introduce the Brain Knowledge Engine (BrainKnow), an automated system designed to extract, link, and synthesize neuroscience knowledge from scientific publications. BrainKnow constructs a comprehensive knowledge graph encompassing 3,626,931 relationships across 37,011 neuroscience concepts, derived from 1,817,744 articles. This vast repository of knowledge is accessible through a user-friendly web interface, facilitating efficient navigation and data retrieval. BrainKnow employs advanced graph network algorithms, specifically Node2Vec, to enhance knowledge recommendation and visualization. This enables users to explore semantic relationships between concepts, predict potential new relationships, and gain a deeper understanding of the interconnectedness within neuroscience. Additionally, BrainKnow ensures real-time updates by synchronizing with PubMed, providing researchers with access to the most current information. BrainKnow serves as a valuable resource for neuroscience researchers, offering a powerful tool for exploring, synthesizing, and leveraging the vast and complex knowledge base of the field.

翻译：神经科学文献的指数级增长给研究人员高效获取和利用相关信息带来了巨大挑战。为解决这一问题，我们引入了神经科学知识引擎（BrainKnow），这是一个旨在从科学出版物中提取、关联和综合神经科学知识的自动化系统。BrainKnow构建了一个全面的知识图谱，涵盖37,011个神经科学概念之间的3,626,931种关系，这些数据源自1,817,744篇文章。这一庞大的知识库可通过用户友好的网络界面访问，便于高效导航和数据检索。BrainKnow采用先进的图网络算法，特别是Node2Vec，以增强知识推荐和可视化功能。这使得用户能够探索概念间的语义关系，预测潜在的新关联，并更深入地理解神经科学领域内部的相互联系。此外，BrainKnow通过与PubMed同步确保实时更新，为研究人员提供最新信息。BrainKnow作为神经科学研究人员的宝贵资源，提供了一个强大的工具，用于探索、综合和利用该领域庞大而复杂的知识库。