论文标题
个人知识基础人口的数据增强
Data Augmentation for Personal Knowledge Base Population
论文作者
论文摘要
冷学知识基础人口（KBP）是从非结构化文档中填充知识库的问题。尽管人工神经网络导致了KBP的不同任务的显着改善，但端到端系统的整体F1仍然很低。这个问题在个人知识基础中更为严重，在数据保护，公平性和隐私方面提出了其他挑战。在这项工作中，我们提出了一个系统，该系统使用基于规则的注释者和图形神经网络缺少链接预测，从而从Tacred数据集中填充更完整，公平和多样化的知识库。
Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant improvements in the different tasks that are part of KBP, the overall F1 of the end-to-end system remains quite low. This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy. In this work, we present a system that uses rule based annotators and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset.
