We will discuss a new system that supports editors creating a database of the features and options available across car models, creating structured data by semi-automated information extraction from lengthy PDF documents.
Edmunds.com is an industry-leading website for car shoppers. To effectively support the car-purchasing process, Edmunds needs to understand the features and options available on the myriad different models offered by manufacturers each year. This critical structured database supports faceted search of models, searching available inventory, and other strategic uses.
This end-to-end capability supports robust processing of unstructured data to identify properties like “air conditioning” and “climate control,” and understand that they are the same underlying feature. For Edmunds, this meant an ~85% reduction in the time it now takes them to get information about a new car model online, from 2 weeks to just 1-2 days. We will also discuss how the NLP models can be re-used across other data, mapping Edmunds’ detailed ontology to a variety of unstructured data sources.