Pinterest is a popular Web application that has over 250 million active users. It is a visual discovery engine for finding ideas for recipes, fashion, weddings, home decoration, and much more. In the last year, the company adopted Semantic Web technologies to create a knowledge graph that aims to represent the vast amount of content and users on Pinterest, to help both content recommendation and ads targeting. In this paper, we present the engineering of an OWL ontology---the Pinterest Taxonomy---that forms the core of Pinterest's knowledge graph, the Pinterest Taste Graph. We describe modeling choices and enhancements to WebProt\'eg\'e that we used for the creation of the ontology. In two months, eight Pinterest engineers, without prior experience of OWL and WebProt\'eg\'e, revamped an existing taxonomy of noisy terms into an OWL ontology. We share our experience and present the key aspects of our work that we believe will be useful for others working in this area.
翻译:兴趣是一个广受欢迎的网络应用程序, 拥有超过2.5亿活跃用户。 它是一个视觉发现引擎, 用于寻找食谱、 时尚、 婚礼、 家庭装饰等的理念。 去年, 该公司采用了语义网络技术来创建知识图, 旨在代表大量内容和用户的意向图, 以帮助提供内容建议和广告目标。 在本文中, 我们介绍OWL 的网络知识- 在线知识学- 兴趣分类- 的工程, 构成Pinterest Taste 图表的核心。 我们描述了我们用来创建肿瘤学的WebProt\'eg\ e 的模型选择和增强。 在两个月里, 8个兴趣工程师, 没有OWL 和WebProt\'e 的先前经验, 将现有的噪音词汇分类系统更新为 OWL 的在线。 我们分享了我们的经验, 并介绍了我们的工作的关键方面, 我们认为这些方面将会对其他人在这方面的工作有用 。