Use of OWL and Semantic Web Technologies at Pinterest
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 WebProtégé that we used for the creation of the ontology. In two months, eight Pinterest engineers, without prior experience of OWL and WebProtégé, 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.
KeywordsPinterest Knowledge graph OWL WebProtégé Ontology engineering Taxonomy
We extend a huge thanks to John Milinovich (prev. at Pinterest), who played a pivotal role in establishing the collaboration between Pinterest and the Protégé team. We also thank Lance Riedel (Pinterest) and Brian Johnson (prev. at Pinterest), who steered the project in its earlier stages. The work described in this paper has been fully supported by Pinterest. Core WebProtégé work is supported by NIH NIGMS Grant GM121724.
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