  Millions of people use online e-commerce platforms to search and buy
products. Identifying attributes in a query is a critical component in
connecting users to relevant items. However, in many cases, the queries have
multiple attributes, and some of them will be in conflict with each other. For
example, the query "maroon 5 dvds" has two candidate attributes, the color
"maroon" or the band "maroon 5", where only one of the attributes can be
present. In this paper, we address the problem of resolving conflicting
attributes in e-commerce queries. A challenge in this problem is that knowledge
bases like Wikipedia that are used to understand web queries are not focused on
the e-commerce domain. E-commerce search engines, however, have access to the
catalog which contains detailed information about the items and its attributes.
We propose a framework that constructs knowledge graphs from catalog to resolve
conflicting attributes in e-commerce queries. Our experiments on real-world
queries on e-commerce platforms demonstrate that resolving conflicting
attributes by leveraging catalog information significantly improves attribute
identification, and also gives out more relevant search results.
