论文标题
使用wikipedia在Yahoo!中使用Wikipedia进行分层图嵌入实体建议知识图
Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph
论文作者
论文摘要
在本文中，我们描述了Wikipedia的一个基于嵌入式的实体推荐框架，该框架将Wikipedia组织成彼此之间分层的图表集合，从其拓扑和内容中学习互补的实体表示，并将它们与轻量级学习对Wikipedia的建议相关实体的方法相结合。通过离线和在线评估，我们表明所得的嵌入和建议在质量和用户参与方面表现良好。平衡简单性和质量，此框架为Yahoo!中的英语和其他语言提供了默认实体建议。知识图，维基百科是其核心子集。
In this paper, we describe an embedding-based entity recommendation framework for Wikipedia that organizes Wikipedia into a collection of graphs layered on top of each other, learns complementary entity representations from their topology and content, and combines them with a lightweight learning-to-rank approach to recommend related entities on Wikipedia. Through offline and online evaluations, we show that the resulting embeddings and recommendations perform well in terms of quality and user engagement. Balancing simplicity and quality, this framework provides default entity recommendations for English and other languages in the Yahoo! Knowledge Graph, which Wikipedia is a core subset of.
