# Use of OWL and Semantic Web Technologies at Pinterest

## TL;DR

Pinterest adopted Semantic Web technologies to develop a comprehensive OWL ontology, the Pinterest Taxonomy, forming the core of their knowledge graph to improve content recommendation and ad targeting.

## Contribution

The paper details the engineering process of creating the Pinterest Taxonomy OWL ontology, including modeling choices and team experience, demonstrating rapid development by engineers new to OWL.

## Key findings

- Successful development of a large OWL ontology in two months
- Eight engineers without prior OWL experience created the ontology
- Enhanced content and user modeling for improved recommendations

## Abstract

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.